A distributed multiple sample testing for massive data

DOI10.1080/02664763.2021.1911967MaRDI QIDQ6157132

Kai Song, Unnamed Author, Jian Shi

Publication date: 19 June 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10128098

zbMATH Keywords

classificationhypothesis testingfraud detectiondistributed scheme

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning

Cites Work

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• Aggregated estimating equation estimation
• The significance probability of the Smirnov two-sample test
• Estimates of the rate of convergence in the two-sided Smirnov criterion
• Distributed testing and estimation under sparse high dimensional models
• Divide and conquer in nonstandard problems and the super-efficiency phenomenon
• Testing the structural stability of temporally dependent functional observations and application to climate projections
• Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data
• Detecting Changes in the Mean of Functional Observations
• Computational Limits of A Distributed Algorithm For Smoothing Spline
• A Scalable Bootstrap for Massive Data
• Nonparametric Bayesian Aggregation for Massive Data

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