Lii-Yuh Leu

St. John's University

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Shanti S. Gupta

Rajshahi University of Engineering and Technology

Jicheng Wu

Henan Academy of Agricultural Sciences

Kuan-Fu Shen

Cheng Ching Hospital

TaChen Liang

Wayne State University

I‐Chin Lin

National Taiwan University

Yen-Chang Chang

National Central University

Ji-Cheng Wu

Yu-Jan Shen

Fuzhou University

TaChen Liang

Wayne State University

Shanthi S. Gupta

Purdue University West Lafayette

Cooperative Institutions

Purdue University West Lafayette

Wayne State University

National Central University

McMaster University

National Taiwan University

Rajshahi University of Engineering and Technology

Government of India

Guru Gobind Singh Indraprastha University

National Informatics Centre

Netaji Subhas University of Technology

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A dynamic bayesian approach to inference from accelerated life tests

Communications in Algebra (1999)

Lii-Yuh Leu Jicheng Wu

Accelerated life testing

10.1080/00927879908826826

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Citations (0)

CONTRIBUTIONS TO MULTIPLE DECISION THEORY

Lii-Yuh Leu

Decision theory

Source

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Citations (3)

A two-stage procedure with accelerated test for selecting the best exponential population

Communication in Statistics- Theory and Methods (2000)

Ji-Cheng Wu Lii-Yuh Leu

Abstract In this article, we discuss a two-stage procedure for selecting the largest location parameter among k(k≥2) two-parameter exponential populations(or products) from an accelerated test. The accelerated test will be conducted at a higher stress level than that of normal in the second stage. under certain assumptions between parameter and stress leveL, the two-stage selection procedure, which guarantees that the probability of correct selection is at least p*, is proposed. At the end of the paper , we present some useful tables that serve as a guide for the needed sample size in the second stage.

10.1080/03610920008832489

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Selection of the Best Normal Population Better than a Control: Dependence Case

Springer eBooks (1988)

Lii-Yuh Leu

10.1007/978-1-4612-3818-8_6

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Selection of the Best with a Preliminary Test for Location-Scale Models

Shanti S. Gupta Lii-Yuh Leu TaChen Liang

Abstract : This paper deals with the problem of selecting the best population from among k(> or = 2) populations which are location-scale models. New selection procedures are proposed for selecting the unique best in terms of the largest location parameter. The procedures include preliminary tests which allow the experimenter to have an option to not select if the statistical evidence is not significant. Two probabilities, the probability to make a selection and the probability of a correct selection, are controlled by these selection procedures. Applications to the normal mean models are considered. Comparisons between the proposed selection procedures and certain earlier existing procedures are also made. Finally, a two-stage procedure for the normal means problem is considered.

10.21236/ada211583

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Selecting the Fairest of kappa (> or = 2) mu-Sided Dice

Shanti S. Gupta Lii-Yuh Leu

Abstract : This paper investigates the problem of selecting, from k(> or =2) m- sided dice, the fairest die. The fairest die is the one corresponding to the smallest (unknown) value of Theta sub i = sum from j = 1 to m of P sub ij - 1/m where p sub ij denotes the jth cell (face) probability for the ith die. The proposed selection procedures are based on Schur convex functions. The problem is studied in the context of the subset selection approach. For small samples case, a method for finding conservative solutions for the selection constants is given. Large sample approximations have also been provided. A related problem of selecting all good populations is also investigated. A procedure for selecting the die with the greatest bias is also proposed and studied. Tables of constants necessary to carry out the procedure for selecting the fairest die are given. Keywords: Subset selection procedures; Multinomial distribution; Best population; Majorization; Schur convex; Schur concave; Good populations.

Dice

Kappa

10.21236/ada212652

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On a Classification Problem: Ranking and Selection Approach

Shanti S. Gupta Lii-Yuh Leu

Abstract : This paper deals with a classification problem based on ranking and selection approach. We assume that the populations follow multivariate normal distribution. The corresponding selection problem is to choose the population with the smallest Mahalanobis distance. The subset selection approach is considered throughout this paper. Sometimes the indifference zone approach is also proposed. It should be pointed out that, for the subset selection approach, we need not assume that the individual to be classified belongs to one of the several given categories. Keywords: Classification rules; Multivariate normal populations; Mahalanobis distance; Ranking and selection; Probability of correct classification.

10.21236/ada211627

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Citations (3)

Design of optimal step–stress accelerated life tests under progressive type I censoring with random removals

Quality & Quantity (2010)

Kuan-Fu Shen Yu-Jan Shen Lii-Yuh Leu

Censoring (clinical trials)

Fisher information

Accelerated life testing

Optimal design

10.1007/s11135-010-9315-y

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Citations (9)

Bayesian Approach for Optimum Step-Stress Accelerated Life Testing

中國統計學報 (2007)

Lii-Yuh Leu Kuan-Fu Shen

This paper presents optimum plans for simple step-stress tests from a Bayes viewpoint. We obtain the optimum test plans to minimize the asymptotic variance of the maximum likelihood estimator of the mean life at a design (use) stress. The emphasis of this paper is to establishment of new areas of application to step-stress accelerated life testing (ALT) and an improvement of existing procedures and theories. Examples for Type Ⅰ and Type Ⅱ censored cases are illustrated. The numerical results indicate that under the same conditions, the proposed Bayesian approach is superior to that of Bai (1989) in the sense that the asymptotic variance is smaller.

Accelerated life testing

10.29973/jcsa.200707.0001

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Citations (5)

Empirical bayes rules for selecting the best exponential population with location parameter

Communication in Statistics- Theory and Methods (1994)

I‐Chin Lin Lii-Yuh Leu

Abstract Among k (≥ 2) exponential populations with location parameters, we use the Empirical Bayes approach to select the best population. We first construct estimators for the unknown prior distribution. We first construct estimators for the unknown prior distribution. Then, the Bayes selection rule with respect to the estimated prior distribution is condidered as the proposed empirical Bayes selection rule. We show the consistency of the estimated prior distribution and establish the asymptotic optimality of the rule . Finally we give an illustrative example to implement the proposed procedure. Keywords: Bayes ruleempirical Bayes ruleasymptotically optimalexponential distributionbest population

Bayes' rule

10.1080/03610929408831355

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Citations (1)