Results 31 to 40 of about 816,450 (327)
Hypothesis Testing for Differentially Private Linear Regression [PDF]
Neural Information Processing Systems, 2022 In this work, we design differentially private hypothesis tests for the following problems in the general linear model: testing a linear relationship and testing for the presence of mixtures.
Daniel Alabi, S. Vadhan
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Differentially Private Hypothesis Testing With the Subsampled and Aggregated Randomized Response Mechanism [PDF]
Statistica sinica, 2022 Randomized response is one of the oldest and most well-known methods for analyzing confidential data. However, its utility for differentially private hypothesis testing is limited because it cannot achieve high privacy levels and low type I error rates ...
V'ictor Pena, Andrés F. Barrientos
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Sichuan jingshen weisheng, 2021 The purpose of this article was to introduce the likelihood ratio test， six nonparametric tests， and the SAS implementation of the survival data. Based on the assumption that the survival data had the exponential distribution， the likelihood ratio test ...
Hu Chunyan, Hu Liangping
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Axioms, 2023 Over the last few decades, the statisticians and reliability analysts have looked at putting exponentiality to the test using the Laplace transform technique.
Mahmoud. E. Bakr +1 more
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An Entropy-Based Approach for Nonparametrically Testing Simple Probability Distribution Hypotheses
Econometrics, 2022 In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on
Ron Mittelhammer +2 more
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NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection
Journal of Statistical Software, 2017 We describe the R package NonpModelCheck for hypothesis testing and variable selection in nonparametric regression. This package implements functions to perform hypothesis testing for the significance of a predictor or a group of predictors in a fully ...
Adriano Zanin Zambom, Michael G. Akritas
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On the Power of Some Nonparametric Isotropy Tests
Acta Universitatis Lodziensis. Folia Oeconomica, 2020 In this paper, properties of nonparametric significance tests verifying the random field isotropy hypothesis are discussed. In particular, the subject of the conducted analysis is the probability of rejecting the null hypothesis when it is true.
Krzysztof Szymoniak-Książek
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Journal of nonparametric statistics (Print), 2023 We provide a new testing procedure to detect serial dependence in time series. Our method is based solely on the ordinal structure of the data. We explicitly allow for ties in the data windows we consider.
C. Weiß, Alexander Schnurr
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Sign, Wilcoxon and Mann-Whitney Tests for Functional Data: An Approach Based on Random Projections
Mathematics, 2020 Sign, Wilcoxon and Mann-Whitney tests are nonparametric methods in one or two-sample problems. The nonparametric methods are alternatives used for testing hypothesis when the standard methods based on the Gaussianity assumption are not suitable to be ...
Rafael Meléndez +2 more
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Nonparametric Hypothesis Tests for Statistical Dependency [PDF]
IEEE Transactions on Signal Processing, 2004 Determining the structure of dependencies among a set of variables is a common task in many signal and image processing applications, including multitarget tracking and computer vision. In this paper, we present an information-theoretic, machine learning approach to problems of this type. We cast this problem as a hypothesis test between factorizations
A.T. Ihler, J.W. Fisher, A.S. Willsky
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