1. A method for nonparametric data analysis without assumptions, comprising: receiving a dataset comprising a plurality of variables; performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships; using a permutation test to determine the significance of the relationships between the variables in the dataset; and generating a report summarizing the results of the nonparametric statistical analysis and permutation test.

2. The method of claim 1, wherein the nonparametric statistical analysis comprises one or more of the Wilcoxon rank-sum test, Kruskal-Wallis test, or Spearman correlation.

3. The method of claim 1, wherein the permutation test comprises randomly permuting the values of one or more variables in the dataset to create a null distribution and computing the p-value of the observed test statistic.

4. The method of claim 1, wherein the report comprises a summary of the significant variables and their relationships, the p-values of the permutation tests, and a visualization of the results.

5. A system for nonparametric data analysis without assumptions, comprising: a data receiver configured to receive a dataset comprising a plurality of variables; a processor configured to execute instructions for: performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships; using a permutation test to determine the significance of the relationships between the variables in the dataset; generating a report summarizing the results of the nonparametric statistical analysis and permutation test; a memory for storing the dataset and the report; and a display device configured to display the report.

6. The system of claim 5, further comprising a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test.

7. The system of claim 5, further comprising a data pre-processing module configured to clean, transform, and normalize the dataset prior to the nonparametric statistical analysis.

8. The system of claim 5, further comprising a feature selection module configured to identify the most relevant variables in the dataset prior to the nonparametric statistical analysis.

9. The system of claim 5, further comprising a clustering module configured to group similar observations in the dataset prior to the nonparametric statistical analysis. System and Method for Nonparametric Data Analysis without Assumptions Abstract The present invention is a system for nonparametric data analysis without assumptions. The system comprises a data receiver, a processor, a memory, and a display device. The processor is configured to execute instructions for performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships. The significance of the relationships between the variables is determined using a permutation test. The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test, which is stored in memory and displayed on the display device. The system also includes a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test. Additionally, the system may include data pre-processing, feature selection, and clustering modules to improve the accuracy and efficiency of the nonparametric statistical analysis. This system provides a powerful and flexible tool for analyzing complex datasets withou , Claims:Claims :

1. A method for nonparametric data analysis without assumptions, comprising: receiving a dataset comprising a plurality of variables; performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships; using a permutation test to determine the significance of the relationships between the variables in the dataset; and generating a report summarizing the results of the nonparametric statistical analysis and permutation test.

2. The method of claim 1, wherein the nonparametric statistical analysis comprises one or more of the Wilcoxon rank-sum test, Kruskal-Wallis test, or Spearman correlation.

3. The method of claim 1, wherein the permutation test comprises randomly permuting the values of one or more variables in the dataset to create a null distribution and computing the p-value of the observed test statistic.

4. The method of claim 1, wherein the report comprises a summary of the significant variables and their relationships, the p-values of the permutation tests, and a visualization of the results.

5. A system for nonparametric data analysis without assumptions, comprising: a data receiver configured to receive a dataset comprising a plurality of variables; a processor configured to execute instructions for: performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships; using a permutation test to determine the significance of the relationships between the variables in the dataset; generating a report summarizing the results of the nonparametric statistical analysis and permutation test; a memory for storing the dataset and the report; and a display device configured to display the report.

6. The system of claim 5, further comprising a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test.

7. The system of claim 5, further comprising a data pre-processing module configured to clean, transform, and normalize the dataset prior to the nonparametric statistical analysis.

8. The system of claim 5, further comprising a feature selection module configured to identify the most relevant variables in the dataset prior to the nonparametric statistical analysis.

9. The system of claim 5, further comprising a clustering module configured to group similar observations in the dataset prior to the nonparametric statistical analysis.

Description:System and Method for Nonparametric Data Analysis without Assumptions
Field of the Invention
[0001] The present invention relates to a system and method for nonparametric data analysis without assumptions. More specifically, the invention provides a novel approach for analyzing data sets that do not conform to the assumptions of traditional parametric statistical tests.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Data analysis is a critical aspect of research and decision-making in various fields, including economics, social sciences, engineering, and many others. Nonparametric data analysis is a method used to analyze data without assuming any specific underlying distribution. Unlike parametric data analysis, nonparametric methods do not require the data to conform to a specific distribution. This makes nonparametric methods more flexible and useful when dealing with complex or ambiguous data sets.
[0004] Traditionally, nonparametric methods have been used to analyze data with a small number of variables. However, as the amount of data being collected increases, nonparametric methods have become more popular for analyzing large and high-dimensional data sets.
[0005] Nonparametric methods are often based on the concept of rank, which refers to the position of an observation relative to the other observations in the data set. For example, if a data set consists of test scores, the rank of a score is determined by its position relative to the other scores.
[0006] One common nonparametric method is the Wilcoxon-Mann-Whitney test, which is used to compare two groups of data to determine if they come from the same distribution. This test does not require the data to follow a specific distribution, making it useful for analyzing data that may not be normally distributed.
[0007] Another nonparametric method is the bootstrap, which involves resampling the data to create multiple data sets. These data sets are then used to estimate statistical properties, such as confidence intervals or the standard error of the mean, without assuming any specific distribution.
[0008] Recently, a new system and method for nonparametric data analysis without assumptions have been developed. This approach involves using a combination of machine learning and nonparametric methods to analyze complex data sets without making any assumptions about the underlying distribution.
[0009] The system and method involve several steps, including data preprocessing, feature selection, and model development. In the data preprocessing step, the data is cleaned and transformed to ensure that it is suitable for analysis. This step can include data normalization, missing data imputation, and outlier detection.
[00010] In the feature selection step, the most important features or variables are selected based on their relevance to the problem being analyzed. This step can help reduce the dimensionality of the data and improve the performance of the analysis.
[00011] Finally, a nonparametric machine learning model is developed using techniques such as decision trees, support vector machines, or random forests. These models are particularly useful for analyzing high-dimensional data sets with complex relationships between variables.
[00012] The system and method for nonparametric data analysis without assumptions have numerous applications in fields such as finance, healthcare, and marketing. In finance, the approach can be used to develop models for predicting stock prices or market trends. In healthcare, the approach can be used to develop models for predicting disease outcomes or patient responses to treatments. In marketing, the approach can be used to analyze consumer behavior and develop predictive models for sales and marketing strategies.
[00013] Overall, the system and method for nonparametric data analysis without assumptions provide a powerful and flexible framework for analyzing complex data sets. The approach enables researchers and analysts to develop models that are both interpretable and accurate, providing a deep understanding of the data and its underlying processes without making any assumptions about the underlying distribution.
[00014] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00015] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00016] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00017] The following paragraphs provide additional support for the claims of the subject application.
[00018] The present invention relates to a system and method for nonparametric data analysis without assumptions. More specifically, the invention provides a novel approach for analyzing data sets that do not conform to the assumptions of traditional parametric statistical tests.
[00019] The method for nonparametric data analysis without assumptions is a powerful tool for analyzing large and complex datasets. The method includes receiving a dataset that comprises a plurality of variables and performing a nonparametric statistical analysis to identify significant variables and their relationships. The method also includes using a permutation test to determine the significance of the relationships between the variables in the dataset.
[00020] The nonparametric statistical analysis can include methods such as the Wilcoxon rank-sum test, Kruskal-Wallis test, or Spearman correlation. These methods do not assume a specific distribution of the data and are therefore more robust than traditional parametric methods. The permutation test involves randomly permuting the values of one or more variables in the dataset to create a null distribution and computing the p-value of the observed test statistic. This allows for a more accurate assessment of the significance of the relationships between the variables in the dataset.
[00021] The method generates a report summarizing the results of the nonparametric statistical analysis and permutation test. The report includes a summary of the significant variables and their relationships, the p-values of the permutation tests, and a visualization of the results. The visualization can include scatterplots, heatmaps, or other types of graphs to help illustrate the relationships between the variables.
[00022] Overall, the method for nonparametric data analysis without assumptions is a powerful tool for analyzing large and complex datasets, especially when traditional parametric methods are not appropriate. It allows for a more accurate assessment of the relationships between variables and can help identify patterns and trends that may not be apparent with traditional methods. The method has applications in a variety of fields, including finance, healthcare, and social media, and can provide valuable insights for decision-making and strategy development.
[00023] A system for nonparametric data analysis without assumptions is a powerful tool for analyzing large and complex datasets. The system includes a data receiver configured to receive a dataset comprising a plurality of variables, and a processor that executes instructions for performing a nonparametric statistical analysis to identify significant variables and their relationships. The significance of the relationships between variables is determined using a permutation test.
[00024] The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test, which is stored in memory and displayed on a display device. The report can include a summary of the significant variables and their relationships, the p-values of the permutation tests, and a visualization of the results.
[00025] The system also includes a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test. This allows users to customize the nonparametric statistical analysis and permutation test, such as selecting different nonparametric methods for analysis, setting significance levels, and selecting different types of permutation tests.
[00026] To ensure accurate results, the system includes a data pre-processing module that cleans, transforms, and normalizes the dataset prior to the nonparametric statistical analysis. The data pre-processing module removes outliers and imputes missing data, and normalizes the data to ensure that all variables are on the same scale.
[00027] Overall, the system for nonparametric data analysis without assumptions is a powerful tool for analyzing large and complex datasets, especially when traditional parametric methods are not appropriate. It allows for a more accurate assessment of the relationships between variables and can help identify patterns and trends that may not be apparent with traditional methods.
[00028] The system has a wide range of applications in various industries, including finance, healthcare, and social media. It can provide valuable insights for decision-making and strategy development, helping companies make data-driven decisions and stay ahead of the competition.
Brief Description of the Drawings
[00029] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00030] FIG. 1 shows an exemplary flowchart exemplifying a method for nonparametric data analysis without assumptions, according to some embodiments of the present disclosure.
[00031] FIG. 2 denotes a representative system for nonparametric data analysis without assumptions, according to some embodiments of the present disclosure.
Detailed Description
[00032] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00033] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00034] The present invention relates to a system and method for nonparametric data analysis without assumptions. More specifically, the invention provides a novel approach for analyzing data sets that do not conform to the assumptions of traditional parametric statistical tests.
[00035] FIG. 1 represents a Nonparametric data analysis method 100 used to analyse data without making any assumptions about the underlying probability distribution. This method is useful when the data does not follow a normal distribution or when the sample size is small. The method involves (at step 102) receiving a dataset comprising a plurality of variables, performing (at step 104) a nonparametric statistical analysis on the dataset to identify significant variables and their relationships, (at step 106) using a permutation test to determine the significance of the relationships between the variables in the dataset, and (at step 108) generating a report summarizing the results of the nonparametric statistical analysis and permutation test.
[00036] In an embodiment, the first step in this method is to receive a dataset comprising a plurality of variables. The dataset could be in any form, such as a CSV file or an Excel sheet. The dataset should contain at least two variables that need to be analysed.
[00037] In an embodiment, the next step is to perform a nonparametric statistical analysis on the dataset to identify significant variables and their relationships. One common nonparametric statistical analysis technique is the Spearman correlation analysis. This analysis identifies the strength and direction of the relationship between two variables. The Spearman correlation coefficient ranges from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation.
[00038] In an embodiment, the nonparametric statistical analysis technique such as the Mann-Whitney U test is used. This test is used to compare two groups of data when the data is not normally distributed. It determines whether there is a significant difference between the medians of the two groups. Using a permutation test to determine the significance of the relationships between the variables in the dataset:
[00039] In an embodiment, once the significant variables and their relationships have been identified, a permutation test is used to determine the significance of the relationships between the variables in the dataset. A permutation test is a nonparametric statistical test that does not require any assumptions about the underlying probability distribution of the data. It works by randomly permuting the values of one or more variables and then calculating the test statistic. This process is repeated many times, and the p-value is calculated as the proportion of test statistics that are more extreme than the observed test statistic.
[00040] In an embodiment, the final step is to generate a report summarizing the results of the nonparametric statistical analysis and permutation test. The report should include a summary of the dataset, the nonparametric statistical analysis performed, the results of the permutation test, and any conclusions that can be drawn from the analysis. The report should also include any visualizations, such as scatter plots or histograms, that help to illustrate the relationships between the variables. For example, suppose a dataset that contains the weight and height of a group of individuals. Several steps are performed to analyse the relationship between weight and height without assuming any underlying probability distribution. First, a Spearman correlation analysis is performed to identify the strength and direction of the relationship between weight and height. We find that there is a strong positive correlation between weight and height (Spearman's correlation coefficient = 0.8). Next, a permutation test is performed to determine the significance of the correlation. Next, randomly permuted the weight and height variables and calculate the test statistic (Spearman's correlation coefficient) 10,000 times. The observed correlation coefficient of 0.8 is statistically significant (p < 0.001). Finally, a report summarizing the results of the analysis is generated. The report includes a summary of the dataset, the results of the Spearman correlation analysis, and the results of Wilcoxon rank-sum test.
[00041] Referring to the preceding embodiment, the nonparametric statistical analysis techniques used in this method, including the Wilcoxon rank-sum test, Kruskal-Wallis test, and Spearman correlation, Wilcoxon rank-sum test can be explained herein. For example, the Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric statistical test used to compare two independent groups of data. It is commonly used when the data is not normally distributed. The test works by ranking the combined data from both groups, calculating the sum of ranks for each group, and then comparing the sums of ranks to determine if there is a significant difference between the groups.
[00042] Referring to the preceding embodiment, the Kruskal-Wallis test is a nonparametric statistical test used to compare three or more independent groups of data. It is commonly used when the data is not normally distributed. The test works by ranking the combined data from all groups, calculating the sum of ranks for each group, and then comparing the sums of ranks to determine if there is a significant difference between the groups.
[00043] FIG. 2 depicts a system 200 for nonparametric data analysis without assumptions is provided for analyzing complex datasets. The system comprises a data receiver 202, a processor 204, a memory 206, and a display device 208. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor is configured to execute instructions for performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships. The significance of the relationships between the variables is determined using a permutation test. The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test, which is stored in memory and displayed on the display device.
[00044] The system includes a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test. The user interface provides a range of options for users to customize the nonparametric statistical analysis and permutation test, such as selecting different nonparametric methods for analysis, setting significance levels, and selecting different types of permutation tests. The user interface also provides the option to view and modify the data prior to analysis.
[00045] In addition to the user interface, the system includes a data pre-processing module that cleans, transforms, and normalizes the dataset prior to the nonparametric statistical analysis. The data pre-processing module provides a range of options for data cleaning, such as removing outliers, imputing missing data, and scaling the data. The module also provides a range of options for data transformation, such as logarithmic transformation, square-root transformation, and Box-Cox transformation. The normalization options include standardization and normalization to a specific range.
[00046] The system further includes a feature selection module that identifies the most relevant variables in the dataset prior to the nonparametric statistical analysis. The feature selection module provides a range of options for selecting the most relevant variables, such as selecting variables with high correlation or selecting variables with high information gain. The module also provides the option to perform dimensionality reduction, such as principal component analysis, to reduce the number of variables.
[00047] Finally, the system includes a clustering module that groups similar observations in the dataset prior to the nonparametric statistical analysis. The clustering module provides a range of options for clustering, such as k-means clustering, hierarchical clustering, and density-based clustering. The module also provides the option to visualize the clusters and select the number of clusters based on the visualization.
[00048] In na example, a pharmaceutical company has collected data from a clinical trial to test the efficacy of a new drug. The dataset includes variables such as age, gender, blood pressure, and cholesterol levels. The company wants to analyze the data to identify significant variables and their relationships without making assumptions about the underlying distribution of the data.
[00049] The company uses the system for nonparametric data analysis without assumptions. The system includes a user interface that allows the company to customize the nonparametric statistical analysis and permutation test. The company selects the Kruskal-Wallis test for the nonparametric statistical analysis and sets the significance level to 0.05. The company also selects the randomization permutation test for the permutation test.
[00050] The system includes a data pre-processing module that cleans, transforms, and normalizes the dataset. The company uses the module to remove outliers and impute missing data. The module also standardizes the data to ensure that all variables are on the same scale.
[00051] The system includes a feature selection module that identifies the most relevant variables in the dataset. The company uses the module to select variables with high correlation and performs principal component analysis to reduce the number of variables.
[00052] Finally, the system includes a clustering module that groups similar observations in the dataset. The company uses the module to perform k-means clustering and visualizes the clusters to select the number of clusters.
[00053] The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test. The report includes a summary of the significant variables and their relationships, as well as the results of the permutation test. The report also includes visualizations of the clusters and the relationships between the significant variables.
[00054] Based on the results of the analysis, the pharmaceutical company discovers that age and cholesterol levels are significant variables that are positively correlated with each other. This means that as age increases, cholesterol levels also increase. The company also discovers that there are two distinct clusters of patients in the clinical trial, one with high blood pressure and high cholesterol levels, and the other with low blood pressure and low cholesterol levels. The pharmaceutical company uses this information to inform future clinical trials and to develop targeted marketing strategies for the new drug.
[00055] A system for nonparametric data analysis without assumptions is provided for analyzing large and complex datasets. The system comprises a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive a dataset comprising a

plurality of variables. The processor is configured to execute instructions for performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships. The significance of the relationships between the variables is determined using a permutation test. The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test, which is stored in memory and displayed on the display device.
[00056] The system includes a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test. The user interface provides a range of options for users to customize the nonparametric statistical analysis and permutation test, such as selecting different nonparametric methods for analysis, setting significance levels, and selecting different types of permutation tests. The user interface also provides the option to view and modify the data prior to analysis.
[00057] To improve the efficiency and accuracy of the nonparametric statistical analysis, the system includes a parallel processing module. The parallel processing module divides the dataset into smaller subsets and performs the nonparametric statistical analysis on each subset in parallel. This reduces the time required for analysis and enables the system to handle large and complex datasets.
[00058] In addition to the parallel processing module, the system includes a data pre-processing module that cleans, transforms, and normalizes the dataset prior to the nonparametric statistical analysis. The data pre-processing module provides a range of options for data cleaning, such as removing outliers, imputing missing data, and scaling the data. The module also provides a range of options for data transformation, such as logarithmic transformation, square-root transformation, and Box-Cox transformation. The normalization options include standardization and normalization to a specific range.
[00059] The system further includes a feature selection module that identifies the most relevant variables in the dataset prior to the nonparametric statistical analysis. The feature selection module provides a range of options for selecting the most relevant variables, such as selecting variables with high correlation or selecting variables with high information gain. The module also provides the option to perform dimensionality reduction, such as principal component analysis, to reduce the number of variables.
[00060] Finally, the system includes a clustering module that groups similar observations in the dataset prior to the nonparametric statistical analysis. The clustering module provides a range of options for clustering, such as k-means clustering, hierarchical clustering, and density-based clustering. The module also provides the option to visualize the clusters and select the number of clusters based on the visualization.
[00061] Referring to preceding embodiment, for example, a financial institution wants to analyze a large dataset comprising customer transactions to identify patterns and relationships without making assumptions about the underlying distribution of the data. The dataset includes variables such as transaction amount, transaction type, transaction date, and customer location.
[00062] The financial institution uses the system for nonparametric data analysis without assumptions. The system includes a user interface that allows the institution to customize the nonparametric statistical analysis and permutation test. The institution selects the Mann-Whitney test for the nonparametric statistical analysis and sets the significance level to 0.01. The institution also selects the bootstrap permutation test for the permutation test.
[00063] The system includes a parallel processing module that divides the dataset into smaller subsets and performs the nonparametric statistical analysis on each subset in parallel. This reduces the time required for analysis and enables the system to handle large and complex datasets. The system includes a data pre-processing module that cleans, transforms, and normalizes the dataset. The institution uses the module to remove outliers and impute missing data. The module also normalizes the data to ensure that all variables are on the same scale.
[00064] The system includes a feature selection module that identifies the most relevant variables in the dataset. The institution uses the module to select variables with high correlation and performs principal component analysis to reduce the number of variables. Finally, the system includes a clustering module that groups similar transactions in the dataset. The institution uses the module to perform density-based clustering and visualizes the clusters to select the number of clusters.
[00065] The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test. The report includes a summary of the significant variables and their relationships, as well as the results of the permutation test. The report also includes visualizations of the clusters and the relationships between the significant variables.
[00066] Based on the results of the analysis, the financial institution discovers that there are two distinct clusters of transactions, one with high transaction amounts and high transaction frequency, and the other with low transaction amounts and low transaction frequency. The institution also discovers that there is a positive correlation between transaction amount and transaction frequency, and that transactions in certain customer locations are more likely to belong to one of the clusters.
[00067] The financial institution uses this information to develop targeted marketing strategies for its customers and to improve its fraud detection systems. A system for nonparametric data analysis without assumptions is provided for analyzing streaming data. The system comprises a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive streaming data comprising a plurality of variables. The processor is configured to execute instructions for performing a nonparametric statistical analysis on the streaming data to identify significant variables and their relationships. The significance of the relationships between the variables is determined using a permutation test. The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test, which is stored in memory and displayed on the display device.
[00068] The system includes a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test. The user interface provides a range of options for users to customize the nonparametric statistical analysis and permutation test, such as selecting different nonparametric methods for analysis, setting significance levels, and selecting different types of permutation tests. The user interface also provides the option to view and modify the streaming data prior to analysis.
[00069] To handle streaming data, the system includes a sliding window module. The sliding window module divides the streaming data into smaller subsets and performs the nonparametric statistical analysis on each subset. The module updates the subsets as new data arrives and removes the oldest subset to maintain a fixed window size.
[00070] In addition to the sliding window module, the system includes a data pre-processing module that cleans, transforms, and normalizes the streaming data prior to the nonparametric statistical analysis. The data pre-processing module provides a range of options for data cleaning, such as removing outliers, imputing missing data, and scaling the data. The module also provides a range of options for data transformation, such as logarithmic transformation, square-root transformation, and Box-Cox transformation. The normalization options include standardization and normalization to a specific range.
[00071] The system further includes a feature selection module that identifies the most relevant variables in the streaming data prior to the non parametric statistical analysis. The feature selection module provides a range of options for selecting the most relevant variables, such as selecting variables with high correlation or selecting variables with high information gain. The module also provides the option to perform dimensionality reduction, such as principal component analysis, to reduce the number of variables.
[00072] Finally, the system includes a clustering module that groups similar observations in the streaming data prior to the nonparametric statistical analysis. The clustering module provides a range of options for clustering, such as k-means clustering, hierarchical clustering, and density-based clustering. The module also provides the option to visualize the clusters and select the number of clusters based on the visualization. For example, a social media platform wants to analyze streaming data from its users to identify trends and relationships without making assumptions about the underlying distribution of the data. The streaming data includes variables such as user activity, user demographics, and user interactions.
[00073] The social media platform uses the system for nonparametric data analysis without assumptions. The system includes a user interface that allows the platform to customize the nonparametric statistical analysis and permutation test. The platform selects the Wilcoxon rank-sum test for the nonparametric statistical analysis and sets the significance level to 0.01. The platform also selects the permutation test with fixed reference for the permutation test.
[00074] The system includes a sliding window module that divides the streaming data into smaller subsets and performs the nonparametric statistical analysis on each subset. The module updates the subsets as new data arrives and removes the oldest subset to maintain a fixed window size. The system includes a data pre-processing module that cleans, transforms, and normalizes the streaming data. The platform uses the module to remove outliers and impute missing data. The module also normalizes the data to ensure that all variables are on the same scale.
[00075] The system includes a feature selection module that identifies the most relevant variables in the streaming data. The platform uses the module to select variables with high correlation and performs principal component analysis to reduce the number of variables. Finally, the system includes a clustering module that groups similar users in the streaming data. The platform uses the module to perform density-based clustering and visualizes the clusters to select the number of clusters.
[00076] The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test. The report includes a summary of the significant variables and their relationships, as well as the results of the permutation test. The report also includes visualizations of the clusters and the relationships between the significant variables.
[00077] Based on the results of the analysis, the social media platform discovers that there are two distinct clusters of users, one with high activity and high interactions, and the other with low activity and low interactions. The platform also discovers that there is a positive correlation between user activity and user interactions, and that users in certain demographics are more likely to belong to one of the clusters.
[00078] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00079] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
 

Claims
I/We Claim:
1. A method for nonparametric data analysis without assumptions, comprising:
receiving a dataset comprising a plurality of variables;
performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships;
using a permutation test to determine the significance of the relationships between the variables in the dataset; and
generating a report summarizing the results of the nonparametric statistical analysis and permutation test.
2. The method of claim 1, wherein the nonparametric statistical analysis comprises one or more of the Wilcoxon rank-sum test, Kruskal-Wallis test, or Spearman correlation.
3. The method of claim 1, wherein the permutation test comprises randomly permuting the values of one or more variables in the dataset to create a null distribution and computing the p-value of the observed test statistic.
4. The method of claim 1, wherein the report comprises a summary of the significant variables and their relationships, the p-values of the permutation tests, and a visualization of the results.
5. A system for nonparametric data analysis without assumptions, comprising:
a data receiver configured to receive a dataset comprising a plurality of variables;
a processor configured to execute instructions for:
performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships;
using a permutation test to determine the significance of the relationships between the variables in the dataset;
generating a report summarizing the results of the nonparametric statistical analysis and permutation test;
a memory for storing the dataset and the report; and
a display device configured to display the report.
6. The system of claim 5, further comprising a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test.
7. The system of claim 5, further comprising a data pre-processing module configured to clean, transform, and normalize the dataset prior to the nonparametric statistical analysis.
8. The system of claim 5, further comprising a feature selection module configured to identify the most relevant variables in the dataset prior to the nonparametric statistical analysis.
9. The system of claim 5, further comprising a clustering module configured to group similar observations in the dataset prior to the nonparametric statistical analysis.

System and Method for Nonparametric Data Analysis without Assumptions
Abstract
The present invention is a system for nonparametric data analysis without assumptions. The system comprises a data receiver, a processor, a memory, and a display device. The processor is configured to execute instructions for performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships. The significance of the relationships between the variables is determined using a permutation test. The system generates a report summarizing the results of the nonparametric statistical analysis and permutation test, which is stored in memory and displayed on the display device. The system also includes a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test. Additionally, the system may include data pre-processing, feature selection, and clustering modules to improve the accuracy and efficiency of the nonparametric statistical analysis. This system provides a powerful and flexible tool for analyzing complex datasets withou , Claims:Claims
I/We Claim:
1. A method for nonparametric data analysis without assumptions, comprising:
receiving a dataset comprising a plurality of variables;
performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships;
using a permutation test to determine the significance of the relationships between the variables in the dataset; and
generating a report summarizing the results of the nonparametric statistical analysis and permutation test.
2. The method of claim 1, wherein the nonparametric statistical analysis comprises one or more of the Wilcoxon rank-sum test, Kruskal-Wallis test, or Spearman correlation.
3. The method of claim 1, wherein the permutation test comprises randomly permuting the values of one or more variables in the dataset to create a null distribution and computing the p-value of the observed test statistic.
4. The method of claim 1, wherein the report comprises a summary of the significant variables and their relationships, the p-values of the permutation tests, and a visualization of the results.
5. A system for nonparametric data analysis without assumptions, comprising:
a data receiver configured to receive a dataset comprising a plurality of variables;
a processor configured to execute instructions for:
performing a nonparametric statistical analysis on the dataset to identify significant variables and their relationships;
using a permutation test to determine the significance of the relationships between the variables in the dataset;
generating a report summarizing the results of the nonparametric statistical analysis and permutation test;
a memory for storing the dataset and the report; and
a display device configured to display the report.
6. The system of claim 5, further comprising a user interface for adjusting the parameters of the nonparametric statistical analysis and permutation test.
7. The system of claim 5, further comprising a data pre-processing module configured to clean, transform, and normalize the dataset prior to the nonparametric statistical analysis.
8. The system of claim 5, further comprising a feature selection module configured to identify the most relevant variables in the dataset prior to the nonparametric statistical analysis.
9. The system of claim 5, further comprising a clustering module configured to group similar observations in the dataset prior to the nonparametric statistical analysis.