☆ 4.6 Article

Asymptotic Distribution-Free Independence Test for High Dimension Data

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2023)

Related references

Note: Only part of the references are listed.

Article Statistics & Probability

Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation

Nabarun Deb et al.

Summary: In this article, a general framework for distribution-free nonparametric testing in multi-dimensions is proposed, utilizing the theory of measure transportation. The approach demonstrates applicability in testing for mutual independence between random vectors and testing for the equality of multivariate distributions. The tests are consistent, computationally feasible, and do not require strong assumptions on the underlying distributions. Additionally, the article contributes new results in measure transportation theory and permutation statistics using Stein's method.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2023)

Add to Collection

Article Statistics & Probability

Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs

Hongjian Shi et al.

Summary: This article introduces a distribution-free and consistent test for testing independence of two random vectors of general dimensions, by combining distance covariance with center-outward ranks and signs. The test is shown to have a limiting null distribution by exploiting the structure of distance covariance and the combinatorial nature of Hallin's ranks and signs. The results suggest that the test is accurate for moderate sample sizes and does not require permutation for implementation.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2022)

Add to Collection

Article Biology

Interpoint-ranking sign covariance for the test of independence

Haeun Moon et al.

Summary: This study generalizes the sign covariance introduced by Bergsma & Dassios (2014) to multivariate random variables and beyond. The new interpoint-ranking sign covariance can be applied to various types of random objects as long as a meaningful similarity measure can be defined, and it equals zero if and only if the two random variables are independent. Numerical experiments and data analyses illustrate the superior empirical performance of the proposed method.

BIOMETRIKA (2022)

Add to Collection

Article Mathematics, Interdisciplinary Applications

Asset splitting algorithm for ultrahigh dimensional portfolio selection and its theoretical property

Zhanrui Cai et al.

JOURNAL OF ECONOMETRICS (2022)

Add to Collection

Article Statistics & Probability

Model-Free Conditional Feature Screening with FDR Control

Zhaoxue Tong et al.

Summary: In this article, a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data is proposed. The method is not constrained by a specific functional form of the regression function and is robust to heavy-tailed responses and predictors.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2022)

Add to Collection

Article Multidisciplinary Sciences

Model-free prediction test with application to genomics data

Zhanrui Cai et al.

Summary: This paper introduces a method for testing the significance of predictors in a regression model under the model-free setting. It assumes that the predictors do not significantly contribute to the prediction of the outcome given confounding variables. By using nonparametric machine learning regression algorithms and comparing the prediction power of different models, the test results can be obtained. The method has important biological implications in gene expression data analysis.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2022)

Add to Collection

Article Mathematical & Computational Biology

Causal discoveries for high dimensional mixed data

Zhanrui Cai et al.

Summary: Causal relationships are important in biological and medical research. This study proposes an algorithm for causal structure estimation using mixed data and demonstrates its effectiveness in high dimensional settings and a real dataset on hepatocellular carcinoma.

STATISTICS IN MEDICINE (2022)

Add to Collection

Article Statistics & Probability

A New Coefficient of Correlation

Sourav Chatterjee

Summary: This article introduces a new coefficient of correlation that satisfies several desirable properties, such as simplicity, interpretability, and consistency under the hypothesis of independence. Unlike existing coefficients in the literature, this new coefficient does not require any assumptions on the distributions of the variables and converges to 0 only when the variables are independent.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2021)

Add to Collection

Article Statistics & Probability

CLASSIFICATION ACCURACY AS A PROXY FOR TWO-SAMPLE TESTING

Ilmun Kim et al.

Summary: The study examines the statistical properties of a two-sample test implicitly performed by data analysts when training classifiers, proving consistency and efficiency of permutation-based and Gaussian-approximation tests across all dimensions. Additionally, it investigates the specialized setting of distinguishing Gaussians with mean-difference S and common covariance Sigma in the high-dimensional context. Furthermore, the study compares the power and efficiency of Fisher's linear discriminant analysis and variants of Hotelling's test in a non-trivial regime while extending results to high-dimensional elliptical distributions with finite kurtosis.

ANNALS OF STATISTICS (2021)

Add to Collection

Article Statistics & Probability

ASYMPTOTIC DISTRIBUTIONS OF HIGH-DIMENSIONAL DISTANCE CORRELATION INFERENCE

Lan Gao et al.

Summary: The paper addresses the underdeveloped issue of asymptotic null distribution for distance correlation in the realistic setting of both sample size and dimensionality diverging. It reveals the blessing of dimensionality phenomenon, showing that the accuracy of normal approximation can increase with dimensionality in high-dimensional distance correlation inference. The study also provides a general theory on the power analysis under the alternative hypothesis of dependence for rescaled distance correlation.

ANNALS OF STATISTICS (2021)

Add to Collection

Article Statistics & Probability

Cauchy Combination Test: A Powerful Test With Analytic p-Value Calculation Under Arbitrary Dependency Structures

Yaowu Liu et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2020)

Add to Collection

Editorial Material Biochemical Research Methods

Single-cell biology: beyond the sum of its parts

Alexander F. Schier

NATURE METHODS (2020)

Add to Collection

Editorial Material Biochemical Research Methods

Single-cell multimodal omics: the power of many

Chenxu Zhu et al.

NATURE METHODS (2020)

Add to Collection

Article Biology

Combining p-values via averaging

Vladimir Vovk et al.

BIOMETRIKA (2020)

Add to Collection

Article Multidisciplinary Sciences

Universal inference

Larry Wasserman et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

Add to Collection

Article Statistics & Probability

DISTANCE-BASED AND RKHS-BASED DEPENDENCE METRICS IN HIGH DIMENSION

Changbo Zhu et al.

ANNALS OF STATISTICS (2020)

Add to Collection

Review Biotechnology & Applied Microbiology

From reads to insight: a hitchhiker's guide to ATAC-seq data analysis

Feng Yan et al.

GENOME BIOLOGY (2020)

Add to Collection

Article Statistics & Probability

Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening

Efang Kong et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2019)

Add to Collection

Review Genetics & Heredity

Integrative single-cell analysis

Tim Stuart et al.

NATURE REVIEWS GENETICS (2019)

Add to Collection

Article Biology

Nonparametric independence testing via mutual information

T. B. Berrett et al.

BIOMETRIKA (2019)

Add to Collection

Review Biochemical Research Methods

Beyond bulk: a review of single cell transcriptomics methodologies and applications

Ashwinikumar Kulkarni et al.

CURRENT OPINION IN BIOTECHNOLOGY (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

A distribution-free test of independence based on mean variance index

Hengjian Cui et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2019)

Add to Collection

Article Statistics & Probability

Global and local two-sample tests via regression

Ilmun Kim et al.

ELECTRONIC JOURNAL OF STATISTICS (2019)

Add to Collection

Article Statistics & Probability

Testing mutual independence in high dimension via distance covariance

Shun Yao et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2018)

Add to Collection

Article Biology

Projection correlation between two random vectors

Liping Zhu et al.

BIOMETRIKA (2017)

Add to Collection

Article Statistics & Probability

BERRY-ESSEEN THEOREMS UNDER WEAK DEPENDENCE

Moritz Jirak

ANNALS OF PROBABILITY (2016)

Add to Collection

Article Statistics & Probability

On some exact distribution-free tests of independence between two random vectors of arbitrary dimensions

Munmun Biswas et al.

JOURNAL OF STATISTICAL PLANNING AND INFERENCE (2016)

Add to Collection

Article Statistics & Probability

Fast Computing for Distance Covariance

Xiaoming Huo et al.

TECHNOMETRICS (2016)

Add to Collection

Article Statistics & Probability

EQUIVALENCE OF DISTANCE-BASED AND RKHS-BASED STATISTICS IN HYPOTHESIS TESTING

Dino Sejdinovic et al.

ANNALS OF STATISTICS (2013)

Add to Collection

Article Biology

A consistent multivariate test of association based on ranks of distances

Ruth Heller et al.

BIOMETRIKA (2013)

Add to Collection

Article Statistics & Probability

The distance correlation t-test of independence in high dimension

Gabor J. Szekely et al.

JOURNAL OF MULTIVARIATE ANALYSIS (2013)

Add to Collection

Article Statistics & Probability

Measuring and testing dependence by correlation of distances

Gabor J. Szekely et al.

ANNALS OF STATISTICS (2007)

Add to Collection

© Peeref 2019-2024. All rights reserved.