Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution
碩士 === 淡江大學 === 數學學系 === 86 === The Bayes sequential estimation problem is to seek an optimal sequential procedure Which includes an optimal stopping time and a Bayes estimator. (i.e. the posterior mean of θ for square error loss.) The...
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ndltd-TW-086TKU014790172015-10-13T17:34:46Z http://ndltd.ncl.edu.tw/handle/03593077172527607183 Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution 貝氏序列下指數分佈的二階段抽樣法 Hwang, Bang-Hwang 黃榜煌 碩士 淡江大學 數學學系 86 The Bayes sequential estimation problem is to seek an optimal sequential procedure Which includes an optimal stopping time and a Bayes estimator. (i.e. the posterior mean of θ for square error loss.) The problem of determining optimal stopping rules is usually more formidable, and although such stopping rules exist under fairly general conditions, their exact determination is usually very difficult. There are many papers that discussed the optimal stopping time. For example, Chow, Robbins and Siegmund (1971) proved the existence of the optimal stopping time for a Bayes sequential estimation problem. Alvo (1977) obtained a lower bound for a Bayes risk of optimal stopping rules for one-parameter exponential family. Of course, there are many papers that discussed the asymptotic efficiency for the stopping time. That is, its Bayes risk compare to the Baye riskof optimal stopping time. In the classical non-Bayesian sequential estimation, Robbins (1959) proposed a sequential procedure and compared its Bayes risk with the Bayes of the optimal fixed sample size procedure in normal case. Replacing of fully sequential procedure, a two stage procedure is given by Ghosh and Mukhopadyay (1981). The asymptotic properties of the two stage procedure were discussed. In the Bayes sequential estimation problem the optimal fixed sample size procedure depends on prior distribution. In case the prior distribution is misspecified, unknown or irrelevant to the present θ, the optiaml fixed sample size can not be obtained. Hwang (1997) proposed a naive sequential procedure and did not depend on the prior distribution such that its Bayes risk was not greater than its Bayes risk of optimal fixed sample size procedure. Instead of the sequential procedure in Hwang (1997), a two stage procedure is proposed in the present paper. The asymptotic properties of procedure are the same as the sequential procedure proposed by Hwang (1997),a two stage procedure is proposed in the present paper. The asymptotic properties of procedure are the same as the sequential procedure proposed by Hwang (1997). Hwang Leng-Cheng 黃連成 1998 學位論文 ; thesis 28 zh-TW |
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碩士 === 淡江大學 === 數學學系 === 86 === The Bayes sequential estimation problem is to seek an
optimal sequential procedure Which includes an optimal stopping
time and a Bayes estimator. (i.e. the posterior mean of θ for
square error loss.) The problem of determining optimal stopping
rules is usually more formidable, and although such stopping
rules exist under fairly general conditions, their exact
determination is usually very difficult. There are many papers
that discussed the optimal stopping time. For example, Chow,
Robbins and Siegmund (1971) proved the existence of the optimal
stopping time for a Bayes sequential estimation problem. Alvo
(1977) obtained a lower bound for a Bayes risk of optimal
stopping rules for one-parameter exponential family. Of course,
there are many papers that discussed the asymptotic efficiency
for the stopping time. That is, its Bayes risk compare to the
Baye riskof optimal stopping time.
In the classical non-Bayesian sequential estimation, Robbins
(1959) proposed a sequential procedure and compared its Bayes
risk with the Bayes of the optimal fixed sample size procedure
in normal case. Replacing of fully sequential procedure, a two
stage procedure is given by Ghosh and Mukhopadyay (1981). The
asymptotic properties of the two stage procedure were discussed.
In the Bayes sequential estimation problem the optimal fixed
sample size procedure depends on prior distribution. In case the
prior distribution is misspecified, unknown or irrelevant to the
present θ, the optiaml fixed sample size can not be obtained.
Hwang (1997) proposed a naive sequential procedure and did not
depend on the prior distribution such that its Bayes risk was
not greater than its Bayes risk of optimal fixed sample size
procedure. Instead of the sequential procedure in Hwang (1997),
a two stage procedure is proposed in the present paper. The
asymptotic properties of procedure are the same as the
sequential procedure proposed by Hwang (1997),a two stage
procedure is proposed in the present paper. The asymptotic
properties of procedure are the same as the sequential procedure
proposed by Hwang (1997).
|
author2 |
Hwang Leng-Cheng |
author_facet |
Hwang Leng-Cheng Hwang, Bang-Hwang 黃榜煌 |
author |
Hwang, Bang-Hwang 黃榜煌 |
spellingShingle |
Hwang, Bang-Hwang 黃榜煌 Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution |
author_sort |
Hwang, Bang-Hwang |
title |
Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution |
title_short |
Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution |
title_full |
Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution |
title_fullStr |
Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution |
title_full_unstemmed |
Two Stage Approach to Bayes Sequential Estimation in the Exponential Distribution |
title_sort |
two stage approach to bayes sequential estimation in the exponential distribution |
publishDate |
1998 |
url |
http://ndltd.ncl.edu.tw/handle/03593077172527607183 |
work_keys_str_mv |
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1717781221107302400 |