Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that t...Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.展开更多
In this paper, we devote to constructing the one-sided empirical Bayes(EB) test for the location parameter in the Gamma distribution by nonparametric method. Under some mild conditions, we prove that the EB test is as...In this paper, we devote to constructing the one-sided empirical Bayes(EB) test for the location parameter in the Gamma distribution by nonparametric method. Under some mild conditions, we prove that the EB test is asymptotically optimal with the rate of the order O(n^(-δs/(2s+1))), where 1/2 ≤ δ < 1 and s > 1 is a given natural number. An example is also given to illustrate that the conditions of the main theorems are easily satisfied.展开更多
The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which ...The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.展开更多
This paper considers tests for regression coefficients in high dimensional partially linear Models.The authors first use the B-spline method to estimate the unknown smooth function so that it could be linearly express...This paper considers tests for regression coefficients in high dimensional partially linear Models.The authors first use the B-spline method to estimate the unknown smooth function so that it could be linearly expressed.Then,the authors propose an empirical likelihood method to test regression coefficients.The authors derive the asymptotic chi-squared distribution with two degrees of freedom of the proposed test statistics under the null hypothesis.In addition,the method is extended to test with nuisance parameters.Simulations show that the proposed method have a good performance in control of type-I error rate and power.The proposed method is also employed to analyze a data of Skin Cutaneous Melanoma(SKCM).展开更多
In this study, the two-sided Empirical Bayes test （EBT） rules for the parameter of continuous one-parameter exponential family with contaminated data （errors in variables） are constructed by a deconvolution kernel...In this study, the two-sided Empirical Bayes test （EBT） rules for the parameter of continuous one-parameter exponential family with contaminated data （errors in variables） are constructed by a deconvolution kernel method. The asymptotically optimal uniformly over a class of prior distributions and uniform rates of convergence, which depends on two types of the error distribu- tions for the proposed EBT rules, are obtained under suitable con- ditions. Finally, an example about the main results of this paper is given.展开更多
The testing covariance equality is of importance in many areas of statistical analysis,such as microarray analysis and quality control.Conventional tests for the finite-dimensional covariance do not apply to high-dime...The testing covariance equality is of importance in many areas of statistical analysis,such as microarray analysis and quality control.Conventional tests for the finite-dimensional covariance do not apply to high-dimensional data in general,and tests for the high-dimensional covariance in the literature usually depend on some special structure of the matrix and whether the dimension diverges.In this paper,we propose a jackknife empirical likelihood method to test the equality of covariance matrices.The asymptotic distribution of the new test is regardless of the divergent or fixed dimension.Simulation studies show that the new test has a very stable size with respect to the dimension and it is also more powerful than the test proposed by Schott(2007)and studied by Srivastava and Yanagihara(2010).Furthermore,we illustrate the method using a breast cancer dataset.展开更多
基金The project is partly supported by NSFC (19971085)the Doctoral Program Foundation of the Institute of High Education and the Special Foundation of Chinese Academy of Sciences.
文摘Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.
基金Supported by the National Natural Science Foundation of China(11671375 and 11471303)Natural Science Foundation of Anhui Provincial Education Department(KJ2017A171)
文摘In this paper, we devote to constructing the one-sided empirical Bayes(EB) test for the location parameter in the Gamma distribution by nonparametric method. Under some mild conditions, we prove that the EB test is asymptotically optimal with the rate of the order O(n^(-δs/(2s+1))), where 1/2 ≤ δ < 1 and s > 1 is a given natural number. An example is also given to illustrate that the conditions of the main theorems are easily satisfied.
基金Supported by the NNSF of China（70471057）Supported by the Natural Science Foundation of the Education Department of Shannxi Province（03JK065）
文摘The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.
基金supported by the University of Chinese Academy of Sciences under Grant No.Y95401TXX2Beijing Natural Science Foundation under Grant No.Z190004Key Program of Joint Funds of the National Natural Science Foundation of China under Grant No.U19B2040。
文摘This paper considers tests for regression coefficients in high dimensional partially linear Models.The authors first use the B-spline method to estimate the unknown smooth function so that it could be linearly expressed.Then,the authors propose an empirical likelihood method to test regression coefficients.The authors derive the asymptotic chi-squared distribution with two degrees of freedom of the proposed test statistics under the null hypothesis.In addition,the method is extended to test with nuisance parameters.Simulations show that the proposed method have a good performance in control of type-I error rate and power.The proposed method is also employed to analyze a data of Skin Cutaneous Melanoma(SKCM).
基金Supported by the Fundamental Research Funds for the Central Universities of China(2013-Ia-040)
文摘In this study, the two-sided Empirical Bayes test （EBT） rules for the parameter of continuous one-parameter exponential family with contaminated data （errors in variables） are constructed by a deconvolution kernel method. The asymptotically optimal uniformly over a class of prior distributions and uniform rates of convergence, which depends on two types of the error distribu- tions for the proposed EBT rules, are obtained under suitable con- ditions. Finally, an example about the main results of this paper is given.
基金supported by the Simons Foundation,National Natural Science Foundation of China(Grant Nos.11771390 and 11371318)Zhejiang Provincial Natural Science Foundation of China(Grant No.LR16A010001)+1 种基金the University of Sydney and Zhejiang University Partnership Collaboration Awardsthe Fundamental Research Funds for the Central Universities.
文摘The testing covariance equality is of importance in many areas of statistical analysis,such as microarray analysis and quality control.Conventional tests for the finite-dimensional covariance do not apply to high-dimensional data in general,and tests for the high-dimensional covariance in the literature usually depend on some special structure of the matrix and whether the dimension diverges.In this paper,we propose a jackknife empirical likelihood method to test the equality of covariance matrices.The asymptotic distribution of the new test is regardless of the divergent or fixed dimension.Simulation studies show that the new test has a very stable size with respect to the dimension and it is also more powerful than the test proposed by Schott(2007)and studied by Srivastava and Yanagihara(2010).Furthermore,we illustrate the method using a breast cancer dataset.
