On the Construction of Multi-Stratum Factorial Designs

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/80930

Title:	On the Construction of Multi-Stratum Factorial Designs
Authors:	李協諭;LI, XIE-YU
Contributors:	統計研究所
Keywords:	最小像差;最佳化設計;粒子群優化演算法;設計鍵矩陣;Minimum Aberration;Optimal Design;PSO;Design Key matrix
Date:	2019-07-12
Issue Date:	2019-09-03 15:17:29 (UTC+8)
Publisher:	國立中央大學
Abstract:	對於無結構實驗單位的因子設計，Fries 與Hunter（1980）提出的最小像差準則被廣泛運用在決定正規部分因子設計上。之後，許多相關研究都集中在擁有多個誤差項的多層因子設計上，這些誤差項來自於複雜的實驗單元結構。Chang 與Cheng (2018) 提出一種貝氏決策準則，它是一個廣義版本的最小像差準則，並且可以運用在最佳化多層因子設計的問題上。對於處理最佳化問題，粒子群優化演算法(PSO) 已是非常流行並被廣泛應用於各種議題。在本篇論文中我們將提出了一個新版本的粒子群優化演算法，來解決正規與非正規多層因子設計的最佳化問題。在正規因子設計下，我們將定義字詞視為粒子群優化演算法中的粒子並將粒子群優化演算法與設計鍵矩陣結合在一起。而在非正規的多層因子設計下，則以處理組合做為演算法中的粒子。;For unstructured experimental units, minimum aberration in Fries and Hunter (1980) is a popular criterion for choosing regular fractional factorial designs. Following which, many related studies focused on multistratum factorial designs with multiple error terms that arise from the complicated structures of experimental units. Chang and Cheng (2018) proposed a Bayesian criterion, which can be considered as a generalized version of the minimum aberration for selecting optimal multi-stratum factorial designs. Particle Swarm Optimization (PSO) algorithm is a popular optimization method that has been widely used in various applications. In this thesis, a new version of PSO is proposed to select regular and nonregular multi-stratum designs. We treat defining words as particles in PSO and link PSO with Design key matrix for selecting regular ones. For nonregular multi-stratum designs, we treat treatment combinations as particles in PSO.
Appears in Collections:	[Graduate Institute of Statistics] Electronic Thesis & Dissertation

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