演講公告

東海大學統計學系學術演講20181127(因故取消)

公告時間:2018-11-15 08:48:51
公告單位:統計學系

 

 東海大學統計學系學術演講 (因故取消)

 

時 間:107年11 月27日(星期二下午14:10~15:00)

 

地 點:管理學院新大樓M243

 

主講人:林長鋆 副教授 (中興大學 統計學研究所)

 

講 題:Stochastic search variable selection for definitive screening designs

           in split-plot and block structures

 

                      Abstract 

Split-plot definitive screening (SPDS) and block definitive screening (BDS) designs have
been developed for detecting active second-order effects in screening
experiments when split-plot and block structures exist. In the literature, the
multistage regression (MSR) and forward stepwise regression (FSR) methods were
proposed for analyzing data for the two types of designs. However, there are
some limitations and potential problems with the regression approaches. First,
the degrees of freedom may not be large enough to estimate all active effects.
Second, the restricted maximum likelihood (REML) estimate for the variances of
whole-plot and block errors can be zero. To overcome these problems and to
enhance the detection capability, we propose a stochastic search variable
selection (SSVS) method based on the Bayesian theory. Different from the
existing Bayesian approaches for split-plot and block designs, the proposed
SSVS method can perform variable selections and choose more reasonable models
which follow the effect heredity principle. The Markov chain Monte Carlo and
Gibbs sampling are applied and a general WinBUGS code that can be used for any
SPDS and BDS designs is provided. Simulation studies are conducted and results
show that the proposed SSVS method well controls the false discovery rate and
has higher detection capability than the regression methods. 

 

Keywords:
Bayesian、effect heredity、false discovery rate、generalized least squares、 Gibbs sampling、

Markov chain Monte Carlof、restricted maximum likelihood、WinBUGS

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