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101 results on '"Chang, Yuan-Tsung"'

3. High-Confident Nonparametric Fixed-Width Uncertainty Intervals and Applications to Projected High-Dimensional Data and Common Mean Estimation

Author

Chang, Yuan-Tsung and Steland, Ansgar

Subjects
Mathematics - Statistics Theory, Statistics - Methodology, 62L10, 62L12, 62G15
Abstract

Nonparametric two-stage procedures to construct fixed-width confidence intervals are studied to quantify uncertainty. It is shown that the validity of the random central limit theorem (RCLT) accompanied by a consistent and asymptotically unbiased estimator of the asymptotic variance already guarantees consistency and first as well as second order efficiency of the two-stage procedures. This holds under the common asymptotics where the length of the confidence interval tends to $0$ as well as under the novel proposed high-confident asymptotics where the confidence level tends to $1$. The approach is motivated by and applicable to data analysis from distributed big data with non-negligible costs of data queries. The following problems are discussed: Fixed-width intervals for a the mean, for a projection when observing high-dimensional data and for the common mean when using nonlinear common mean estimators under order constraints. The procedures are investigated by simulations and illustrated by a real data analysis.

Published
2019

4. Jackknife variance estimation for common mean estimators under ordered variances and general two-sample statistics

Author

Steland, Ansgar and Chang, Yuan-Tsung

Subjects
Mathematics - Statistics Theory, Statistics - Applications
Abstract

Samples with a common mean but possibly different, ordered variances arise in various fields such as interlaboratory experiments, field studies or the analysis of sensor data. Estimators for the common mean under ordered variances typically employ random weights, which depend on the sample means and the unbiased variance estimators. They take different forms when the sample estimators are in agreement with the order constraints or not, which complicates even basic analyses such as estimating their variance. We propose to use the jackknife, whose consistency is established for general smooth two--sample statistics induced by continuously G\^ateux or Fr\'echet differentiable functionals, and, more generally, asymptotically linear two--sample statistics, allowing us to study a large class of common mean estimators. Further, it is shown that the common mean estimators under consideration satisfy a central limit theorem (CLT). We investigate the accuracy of the resulting confidence intervals by simulations and illustrate the approach by analyzing several data sets., Comment: 37 pages

Published
2017

7. Performing Iris Segmentation by Using Geodesic Active Contour (GAC)

Author

Chang, Yuan-Tsung, Ou, Chih-Wen, Shih, Timothy K., Li, Yung-Hui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Hui, Lin, editor

Published
2018
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10. Simultaneous estimation of Poisson means in two-way contingency tables under normalized squared error loss in multiplicative models (Bayesian approaches and statistical inference)

Author

Chang, Yuan-Tsung and Shinozaki, Nobuo

Abstract

Shrinkage estimation of Poisson means is considered when observations are given in the form of a two-way contingency table. Assuming a multiplicative Poisson model, estimators which shrink to the specified values or an order statistic in one dimension and in two dimensions are considered and are shown to dominate the maximum likelihood estimator (MLE) under normalized squared error loss.

Published
2022

11. Pitman closeness domination in predictive density estimation for two-ordered normal means under $$\alpha$$α-divergence loss

Author

Chang, Yuan-Tsung, Shinozaki, Nobuo, and Strawderman, William

Abstract

We consider Pitman closeness domination in predictive density estimation problems when the underlying loss metric is $$\alpha$$α-divergence, $$\{D(\alpha )\}$${D(α)}, a loss introduced by Csiszàr (Stud Sci Math Hung 2:299–318, 1967). The underlying distributions considered are normal location-scale models, including the distribution of the observables, the distribution of the variable, whose density is to be predicted, and the estimated predictive density which will be taken to be of the plug-in type. The scales may be known or unknown. Chang and Strawderman (J Multivar Anal 128:1–9, 2014) have derived a general expression for the $$\alpha$$α-divergence loss in this setup, and have shown that it is a concave monotone function of quadratic loss, and also a function of the variances (predicand, and plug-in). We demonstrate $$\{D(\alpha )\}$${D(α)}Pitman closeness domination of certain plug-in predictive densities over others for the entire class of metrics simultaneously when modified Pitman closeness domination holds in the related problem of estimating the mean. We also establish $$\{D(\alpha )\}$${D(α)}Pitman closeness results for certain generalized Bayesian (best invariant) predictive density estimators. Examples of $$\{D(\alpha )\}$${D(α)}Pitman closeness domination presented relate to the problem of estimating the predictive density of the variable with the larger mean. We also consider the case of two-ordered normal means with a known covariance matrix.

Published
2024
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15. A class of general pretest estimators for the univariate normal mean.

Author

Shih, Jia-Han, Konno, Yoshihiko, Chang, Yuan-Tsung, and Emura, Takeshi

Subjects
BAYES' estimation, DISTRIBUTION (Probability theory), STATISTICAL decision making, DECISION theory, PROBABILITY theory, FRUIT drying
Abstract

In this paper, we propose a class of general pretest estimators for the univariate normal mean. The main mathematical idea of the proposed class is the adaptation of randomized tests, where the randomization probability is related to a shrinkage parameter. Consequently, the proposed class includes many existing estimators, such as the pretest, shrinkage, Bayes, and empirical Bayes estimators as special cases. Furthermore, the proposed class can be easily tuned for users by adjusting significance levels and probability function. We derive theoretical properties of the proposed class, such as the expressions for the distribution function, bias, and MSE. Our expressions for the bias and MSE turn out to be simpler than those previously derived for some existing formulas for special cases. We also conduct simulation studies to examine our theoretical results and demonstrate the application of the proposed class through a real dataset. [ABSTRACT FROM AUTHOR]

Published
2023
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38. New types of shrinkage estimators of Poisson means under the normalized squared error loss.

Author

Chang, Yuan-Tsung and Shinozaki, Nobuo

Subjects
*ISOTONIC regression, *ORDER statistics, *ERROR
Abstract

In estimating p(⩾ 2) independent Poisson means, Clevenson and Zidek (1975) have proposed a class of estimators that shrink the unbiased estimator to the origin and dominate the unbiased one under the normalized squared error loss. This class of estimators was subsequently enlarged in several directions. This article deals with the problem and proposes new classes of dominating estimators using prior information pertinently. Dominance is shown by partitioning the sample space into disjoint subsets and averaging the loss difference over each subset. Estimation of several Poisson mean vectors is also discussed. Further, simultaneous estimation of Poisson means under order restriction is treated and estimators which dominate the isotonic regression estimator are proposed for some types of order restrictions. [ABSTRACT FROM AUTHOR]

Published
2019
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40. Two-stage james-stein estimators of the mean based on prior knowledge

Author

Chang Yuan-Tsung

Subjects
Statistics and Probability, Sampling scheme, Sample size determination, Statistics, James–Stein estimator, Estimator, Mean vector, Sampling (statistics), Two sample, Stage (hydrology), Mathematics
Abstract

We suggest five types of two-stage James-Stein type estimators of the mean vector μ based on prior knowledge about μ and two-stage sampling scheme proposed by Waikar and Katti(1971) Their risks are evaluated and calculated to compare with two-stage estimator suggested by Waikar and Katti(1971) when the prior form of an initial estimate of μ is 0. We find that the five estimators suggested here all have high efficiencies in large dimensions and/or in large value of ratio of two sample sizes at each stage.

Published
1995

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