arXiv subjects

Ming-Tien Tsai

Publications and source records attributed to Ming-Tien Tsai.

7 recordsLinked to original sources

arXiv2024

Towards a unified theory for testing statistical hypothesis: Multinormal mean with nuisance covariance matrix

Under a multinormal distribution with an arbitrary unknown covariance matrix, the main purpose of this paper is to propose a framework to achieve the goal of reconciliation of Bayesian, frequentist, and Fisher's reporting $p$-values, Neyman-Pearson's optimal theory and Wald's decision theory for the problems of testing mean against restricted alternatives (closed convex cones). To proceed, the tests constructed via the likelihood ratio (LR) and the union-intersection (UI) principles are studied. For the problems of testing against restricted alternatives, first, we show that the LRT and the UIT are not the proper Bayes tests, however, they are shown to be the integrated LRT and the integrated UIT, respectively. For the problem of testing against the positive orthant space alternative, both the null distributions of the LRT and the UIT depend on the unknown nuisance covariance matrix. Hence we have difficulty adopting Fisher's approach to reporting $p$-values. On the other hand, according to the definition of the level of significance, both the LRT and the UIT are shown to be power-dominated by the corresponding LRT and UIT for testing against the half-space alternative, respectively. Hence, both the LRT and the UIT are $α$-inadmissible, these results are against the common statistical sense. Neither Fisher's approach of reporting $p$-values alone nor Neyman-Pearson's optimal theory for power function alone is a satisfactory criterion for evaluating the performance of tests. Wald's decision theory via $d$-admissibility may shed light on resolving these challenging issues of imposing the balance between type 1 error and power.

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arXiv2024

On the consistent estimators of the population covariance matrix and its reparameterizations

For the high-dimensional covariance estimation problem, when $\lim_{n\to \infty}p/n=c \in (0,1)$ the orthogonally equivariant estimator of the population covariance matrix proposed by Tsai and Tsai (2024b) enjoys some optimal properties. Under some regularity conditions, they showed that their novel estimators of eigenvalues are consistent with the eigenvalues of the population covariance matrix. In this note, first, we show that their novel estimator is a consistent estimator of the population covariance matrix under a high-dimensional asymptotic setup. Moreover, we may show that the novel estimator is the MLE of the population covariance matrix when $c \in (0, 1)$. The novel estimator is incorporated to establish the optimal decomposite $T_{T}^{2}-$test for a high-dimensional statistical hypothesis testing problem and to make the statistical inference for the high-dimensional principal component analysis-related problems without the sparsity assumption. Some remarks when $p >n $, especially for the high-dimensional low-sample size categorical data models $p >> n$, are made in the final section.

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arXiv2024

On the orthogonally equivariant estimators of a covariance matrix

In this note, when the dimension $p$ is large we look into the insight of the Mar$\check{c}$enko-Pastur equation to get an explicit equality relationship, and use the obtained equality to establish a new kind of orthogonally equivariant estimator of the population covariance matrix. Under some regularity conditions, the proposed novel estimators of the population eigenvalues are shown to be consistent for the eigenvalues of population covariance matrix. It is also shown that the proposed estimator is the best orthogonally equivariant estimator of population covariance matrix under the normalized Stein loss function.

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arXiv2024

The decomposite $T^{2}$-test when the dimension is large

In this paper, we discuss tests for mean vector of high-dimensional data when the dimension $p$ is a function of sample size $n$. One of the tests, called the decomposite $T^{2}$-test, in the high-dimensional testing problem is constructed based on the estimation work of Ledoit and Wolf (2018), which is an optimal orthogonally equivariant estimator of the inverse of population covariance matrix under Stein loss function. The asymptotic distribution function of the test statistic is investigated under a sequence of local alternatives. The asymptotic relative efficiency is used to see whether a test is optimal and to perform the power comparisons of tests. An application of the decomposite $T^{2}$-test is in testing significance for the effect of monthly unlimited transport policy on public transportation, in which the data are taken from Taipei Metro System.

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arXiv2019

Towards an unified theory for testing statistical hypothesis: Multinormal mean with nuisance covariance matrix

Under a multinormal distribution with arbitrary unknown covariance matrix, the main purpose of this paper is to propose a framework to achieve the goal of reconciliation of Bayesian, frequentist and Fisherian paradigms for the problems of testing mean against restricted alternatives (closed convex cones). Combining Fisher's fiducial inference and Wald's decision theory via d-admissibility into an unified approach, the goal can then be achieved. To proceed, the tests constructed via the union-intersection principle are studied.

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arXiv2017

A note on MLE of covariance matrix

For a multivariate normal set up, it is well known that the maximum likelihood estimator of covariance matrix is neither admissible nor minimax under the Stein loss function. For the past six decades, a bunch of researches have followed along this line for Stein's phenomenon in the literature. In this note, the results are two folds: Firstly, with respect to Stein type loss function we use the full Iwasawa decomposition to enhance the unpleasant phenomenon that the minimum risks of maximum likelihood estimators for the different coordinate systems (Cholesky decomposition and full Iwasawa decomposition) are different. Secondly, we introduce a new class of loss functions to show that the minimum risks of maximum likelihood estimators for the different coordinate systems, the Cholesky decomposition and the full Iwasawa decomposition, are of the same, and hence the Stein's paradox disappears.

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arXiv2017

Admissibility of invariant tests for means with covariates

For a multinormal distribution with a $p$-dimensional mean vector ${\mbtheta}$ and an arbitrary unknown dispersion matrix ${\mbSigma}$, Rao ([9], [10]) proposed two tests for the problem of testing $ H_{0}:{\mbtheta}_{1} = {\bf 0}, {\mbtheta}_{2} = {\bf 0}, {\mbSigma}~ \hbox{unspecified},~\hbox{versus}~H_{1}:{\mbtheta}_{1} \ne {\bf 0}, {\mbtheta}_{2} ={\bf 0}, {\mbSigma}~\hbox{unspecified}$, where ${\mbtheta}^{'}=({\mbtheta}^{'}_{1},{\mbtheta}^{'}_{2})$. These tests are referred to as Rao's $W$-test (likelihood ratio test) and Rao's $U$-test (union-intersection test), respectively. This work is inspired by the well-known work of Marden and Perlman [6] who claimed that Hotelling's $T^{2}$-test is admissible while Rao's $U$-test is inadmissible. Both Rao's $U$-test and Hotelling's $T^{2}$-test can be constructed by applying the union-intersection principle that incorporates the information ${\mbtheta}_{2}={\bf 0}$ for Rao's $U$-test statistic but does not incorporate it for Hotelling's $T^{2}$-test statistic. Rao's $U$-test is believed to exhibit some optimal properties. Rao's $U$-test is shown to be admissible by fully incorporating the information ${\mbtheta}_{2}={\bf 0}$, but Hotelling's $T^{2}$-test is inadmissible.

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