Data depth-based nonparametric tests for multivariate scales

DOI10.1007/s42519-021-00236-6zbMath1478.62103OpenAlexW4210431494MaRDI QIDQ2074660

Somanath D. Pawar, Digambar T. Shirke

Publication date: 10 February 2022

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s42519-021-00236-6

zbMATH Keywords

permutation testsdata depthmultivariate scales tests

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Robustness and adaptive procedures (parametric inference) (62F35) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01)

Cites Work

• On a notion of data depth based on random simplices
• Multivariate analysis by data depth: Descriptive statistics, graphics and inference. (With discussions and rejoinder)
• On distribution-free tests for the multivariate two-sample location-scale model
• A nonparametric multivariate multisample test based on data depth
• New nonparametric tests of multivariate locations and scales using data depth
• Nonparametric Tests for Homogeneity of Species Assemblages: A Data Depth Approach
• Data depth-based nonparametric scale tests
• Control Charts for Multivariate Processes
• On a Geometric Notion of Quantiles for Multivariate Data
• Rank-Sum Tests for Dispersions
• Regression Depth
• A Quality Index Based on Data Depth and Multivariate Rank Tests
• DD-Classifier: Nonparametric Classification Procedure Based onDD-Plot
• Nonparametric tests for multivariate multi-sample locations based on data depth
• New Nonparametric Tests for Comparing Multivariate Scales Using Data Depth
• Percentile Modifications of Two Sample Rank Tests
• A GENERAL DISTRIBUTION THEORY FOR A CLASS OF LIKELIHOOD CRITERIA
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