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JSRI 2014, 10(2): 181-196 Back to browse issues page
An Efficient Bayesian Optimal Design for Logistic Model
H. Talebi *1, D. Poursina
1- , h-talebi@sci.ui.ac.ir
Abstract:   (3334 Views)

Consider a Bayesian optimal design with many support points which poses the problem of collecting data with a few number of observations at each design point. Under such a scenario the asymptotic property of using Fisher information matrix for approximating the covariance matrix of posterior ML estimators might be doubtful. We suggest to use Bhattcharyya matrix in deriving the information matrix, led to modified Bayesian D-optimal criterion. This criterion is used to obtain optimal design for logistic model. It is shown that the resulting optimal design is more efficient than design given by Chaloner and Larentz (1989) using ordinary Bayesian D-optimal criterion.

Keywords: Bhattcharyya matrix, logistic model, design support points, Bayesian D-optimal criterion, Bayesian optimal design.
Full-Text [PDF 426 kb]   (751 Downloads)    
Type of Study: Research | Subject: General
Accepted: 2015/12/10 | Published: 2016/09/14
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Talebi H, Poursina D. An Efficient Bayesian Optimal Design for Logistic Model. JSRI. 2014; 10 (2) :181-196
URL: http://jsri.srtc.ac.ir/article-1-48-en.html

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Volume 10, Issue 2 (3-2014) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران Journal of Statistical Research of Iran JSRI
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