Daojiang He

Anhui Normal University

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Mingxiang Cao

Anhui Normal University

Lei He

Anhui Normal University

Kai Xu

Anhui Normal University

Xingzhong Xu

Hubei University

Yunpeng Wang

Shanghai Jiao Tong University

Dongchu Sun

East China Normal University

Kai Xu

National University of Defense Technology

Xuhua Liu

Anhui Science and Technology University

Tao Lu

University of Nevada, Reno

Rong‐Xian Yue

Shanghai Normal University

Cooperative Institutions

Anhui Normal University

Chinese Academy of Sciences

Beijing Institute of Technology

East China Normal University

University of Science and Technology of China

Shanghai Jiao Tong University

Beijing University of Technology

University of Chinese Academy of Sciences

Shandong University

Zhejiang University

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Estimation of the Cholesky decomposition in a conditional independent normal model with missing data

Statistics & Probability Letters (2014)

Daojiang He Kai Xu

Conditional expectation

Restricted maximum likelihood

10.1016/j.spl.2014.01.028

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Citations (3)

A High-Dimensional Test for Multivariate Analysis of Variance Under a Low-Dimensional Factor Structure

Communications in Mathematics and Statistics (2021)

Mingxiang Cao Yanling Zhao Kai Xu Daojiang He Xudong Huang

Statistic

Factor (programming language)

Null (SQL)

Goldfeld–Quandt test

10.1007/s40304-020-00236-1

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Citations (1)

Objective Bayesian hypothesis testing and estimation for the intraclass model

Statistical Theory and Related Fields (2018)

Duo Zhang Daojiang He Xiaoqian Sun Tao Lu Min Wang

The intraclass correlation coefficient (ICC) plays an important role in various fields of study as a coefficient of reliability. In this paper, we consider objective Bayesian analysis for the ICC in the context of normal linear regression model. We first derive two objective priors for the unknown parameters and show that both result in proper posterior distributions. Within a Bayesian decision-theoretic framework, we then propose an objective Bayesian solution to the problems of hypothesis testing and point estimation of the ICC based on a combined use of the intrinsic discrepancy loss function and objective priors. The proposed solution has an appealing invariance property under one-to-one reparametrisation of the quantity of interest. Simulation studies are conducted to investigate the performance the proposed solution. Finally, a real data application is provided for illustrative purposes.

Point estimation

Bayes estimator

10.1080/24754269.2018.1481586

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Citations (1)

A high-dimensional test for the k-sample Behrens–Fisher problem

Journal of nonparametric statistics (2022)

Daojiang He Huijun Shi Kai Xu Mingxiang Cao

In this paper, the problem of testing the equality of the mean vectors of k populations with possibly unknown and unequal covariance matrices is investigated in high-dimensional settings. The null distributions of most existing tests are asymptotically normal which inevitably imposes strong conditions on covariance matrices. However, we assume here only mild additional conditions on the proposed test, which offers much flexibility in practical applications. Additionally, the Welch–Satterthwaite χ2-approximation we adopted can automatically mimic the shape of the null distribution of the proposed test statistic, while the normal approximation cannot achieve the adaptivity. Finally, an extensive simulation study shows that the proposed test has better performance on both size and power compared with existing methods.

Null (SQL)

Statistic

10.1080/10485252.2022.2147172

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Citations (0)

Linearly admissible estimators on linear functions of regression coefficient under balanced loss function

Communication in Statistics- Theory and Methods (2019)

Mingxiang Cao Daojiang He

Linearly admissible estimators on linear functions of regression coefficient are studied in a singular linear model and balanced loss when the design matrix has not full column rank. The sufficient and necessary conditions for linear estimators to be admissible are obtained respectively in homogeneous and inhomogeneous classes.

Influence function

10.1080/03610926.2018.1472788

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Citations (2)

Further results on estimation of covariance matrix

Statistics & Probability Letters (2015)

Kai Xu Daojiang He

Matrix (chemical analysis)

10.1016/j.spl.2015.02.022

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Citations (6)

R-optimal designs for individual prediction in random coefficient regression models

Statistics & Probability Letters (2020)

Lei He Daojiang He

10.1016/j.spl.2019.108684

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Citations (4)

Notes on Admissibility of Linear Estimators under Matrix Loss Function

Journal of Jilin University(Science Edition) (2009)

Daojiang He

We investigated the admissibility of linear estimators in a linear model under matrix loss function.Based on the thoery of matrix,some new necessary and sufficient conditions for a linear estimator to be admissible in the class of linear estimators were obtained.The new conditions are demonstrated to be much easier to verify and to apply via an example.

Matrix (chemical analysis)

Source

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Linear hypothesis testing in high-dimensional heteroscedastics via random integration

arXiv (Cornell University) (2024)

Mingxiang Cao Hongwei Zhang Kai Xu Daojiang He

In this paper, for the problem of heteroskedastic general linear hypothesis testing (GLHT) in high-dimensional settings, we propose a random integration method based on the reference L2-norm to deal with such problems. The asymptotic properties of the test statistic can be obtained under the null hypothesis when the relationship between data dimensions and sample size is not specified. The results show that it is more advisable to approximate the null distribution of the test using the distribution of the chi-square type mixture, and it is shown through some numerical simulations and real data analysis that our proposed test is powerful.

10.48550/arxiv.2409.12066

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Testing Homogeneity of Inverse Gaussian Scale Parameters Based on Generalized Likelihood Ratio

Communications in Statistics - Simulation and Computation (2012)

Xuhua Liu Daojiang He

Abstract This article presents a new procedure for testing homogeneity of scale parameters from k independent inverse Gaussian populations. Based on the idea of generalized likelihood ratio method, a new generalized p-value is derived. Some simulation results are presented to compare the performance of the proposed method and existing methods. Numerical results show that the proposed test has good size and power performance. Keywords: Generalized likelihood ratioGeneralized p-valueInverse Gaussian distributionMathematics Subject Classification: 62F03

Inverse Gaussian distribution

Generalized inverse

Score test

10.1080/03610918.2011.650257

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Citations (9)