臺灣博碩士論文加值系統

本文主要在探討下列「多重統計決策」問題
A:實驗設計
B:可靠度分析
C:迴歸分析
首先，針對「事前情報」不夠完整的情況，我們可利用「T-最適準則」，在合理的決策方案中，決定最適當的樣本數，以便從 一些相互對抗的母群體中，選出「最好」或「較好」的母群體。
其次，本又提出「局部最適準則」。針對挑選較好母群體的策問題，我們利用此準則，可在控制「不正確決策機率」下，造出最育”效率”的決策方案，甚至，亦可解決樣本數不等的問題。進而，由多重決策理論的觀點，來解決傳統檢定方法的一些困難。

This dissertation emphasizes ranking and selection approach to multiple statistical decision problems in the following areas:
PART A: Design of experiments.
PART B: Reliability analysis.
PART C: Regression analysis.
For the incomplete prior information decision problems, I-optimal criterion, which is the compromise of minimax criterion and Bayes criterion, has received great attention. By employing this criterion for the class of natural selection rules, it allows us to determine optimal sample size for selecting the best population or the better populations among several competing populations for PART A. B and C respectively.
Next, "local optimality" is also discussed. Recently, the attention has been increasingly given to the construction of optimal decision rules for selecting all good populations that aresuperior to a control population. Some investigations have been studied for the unequal sample size case. But the optimality has seldom been considered. By using "local optimality", it allows usto control the error probabilities and maximize the efficiency in picking out the superior populations even for the unequal sample size case. Furthermore, hypothesis testing approach is also studied　　 from the selection point of view. Our approach can solve some difficulties which arise from the traditional methods.

TABLE OF CONTENTS
ABSTRACT
ACKNOWLEDGEMENTS
CHAPTER 1. Introduction
CHATPER 2. P-optimal decision procedures for selecting the best population in randomized complete block design
2.1 Introduction
2.2 Basic formulation of the selection problem
目次:2.3 Probability of correct selection
2.4 P-optimal sample size
2.5 Numerical example
2.6 Relationship between and n*
2.7 Discussion
CHATPER 3. Most economical procedure for selecting good normal populations
3.1 Introduction
3.2 Basic formulation
3.3 Expression of y(ρ;δn）
3.4 Equal sample size case
CAHPTER 4. Selecting the most reliable Weibull poulations
4.1 Introduction
4.2 Notations and formulation of the problem
4.3 Expresion of pi(θ）
4.4 P-optimal sample size
4.5 Numerical example
4.6 β1,.....βk are not equal case
CHAPTER 5. Most economial procedures for more relible Weibull population
5.1 Introduction
5.2 Basic formulation
5.3 Expresion of pi(θi）
5.4 P-optimal sample size
5.5 Equal sample size case
CHAPTER 6. Selecting the largest slope in simple linear regression model
6.1 Introduction
6.2 Notations and basic formulation
6.3 The expression of probobility of correct selection
6.4 P-optimal decision rule
CHAPTER 7. Some locally optimal selection rules
7.1 Introduction
7.2 Comparison with a control
7.3 Application to specal cases
References
中文摘要
研究生個人資料
口試合格證明