※ [本文轉錄自 NCTU-STAT97G 看板]
作者: pei16 (^^) 看板: NCTU-STAT97G
標題: [演講公告] 12/25 統計所專題演講
時間: Mon Dec 21 22:34:46 2009
交通大學、清華大學 統計學研究所 專題演講
題 目：D-optimal Partially Replicated Two-Level Factorial Designs
主講人：廖振鐸教授（臺灣大學農藝所生物統計組）
時 間：98年12月25日(星期五)上午11:10-12:00
(上午10:50-11:10茶會於交大統計所429室舉行)
地 點：交大綜合一館427室
Abstract
At the early stages of a factorial experiment, unreplicated fractional
two-level designs are commonly used to identify important or active effects.
Under the situation that there is no prior information available on which
effects might be active, minimum aberration designs may serve as reasonable
choices for gaining more information about a large set of potential effects.
However, the analysis methods for unreplicated data may perform
unsatisfactorily in identifying truly active effects, particularly when the
effect sparsity principle does not hold. This is due mainly to the lack of a
replication-based estimate of the error variance. Therefore, when the prior
information is provided and the set of possibly active effects contains all
the potential effects. We may first find an economical design, not
necessarily a minimum aberration design, for estimating the specified
possibly active effects. If some additional runs remain, then we can consider
running repeated treatment combinations to obtain a realistic estimate of
experimental error, which is used to test whether the specified possibly
active effects are truly active. The partially replicated two-level factorial
designs usually work well regardless of the effect sparsity. In this talk, we
will discuss D-optimal partially replicated designs derived from
parallel-flats designs and Hadamard matrices.
Keywords: Parallel-flats design; Hadamard matrix; orthogonal array;
projection property; pure error.
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