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| Author | : Yimin Ma |
| Publisher | : |
| Total Pages | : 54 |
| Release | : 1994 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
| Author | : S. S. Gupta |
| Publisher | : |
| Total Pages | : 31 |
| Release | : 1993 |
| Genre | : |
| ISBN | : |
| Author | : Shanti S. Gupta |
| Publisher | : |
| Total Pages | : 25 |
| Release | : 1998 |
| Genre | : |
| ISBN | : |
In this paper we investigate the problem of selecting the best logistic population from k(greater than or equal 2) possible candidates. The selected population must also be better than a given control. We employ the empirical Bayes approach and develop a selection procedure. The performance (rate of convergence) of the proposed selection rule is also analyzed. We also carry out a simulation study to investigate the rate of convergence of the proposed empirical Bayes selection procedure. The results of simulation are provided in the paper.
| Author | : |
| Publisher | : |
| Total Pages | : 744 |
| Release | : 1998 |
| Genre | : Statistics |
| ISBN | : |
| Author | : Shanti S. Gupta |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 1998 |
| Genre | : |
| ISBN | : |
In this paper we investigate the problem of selecting the best logistic population from k(greater than or equal 2) possible candidates. The selected population must also be better than a given control. We employ the empirical Bayes approach and develop a selection procedure. The performance (rate of convergence) of the proposed selection rule is also analyzed. We also carry out a simulation study to investigate the rate of convergence of the proposed empirical Bayes selection procedure. The results of simulation are provided in the paper.
| Author | : Shanti S. Gupta |
| Publisher | : |
| Total Pages | : 24 |
| Release | : 1986 |
| Genre | : |
| ISBN | : |
Some selection rules based on monotone empirical Bayes estimators of the binomial parameters are proposed. First, it is shown that, under the squared error loss, the Bayes risks of the proposed monotone empirical Bayes estimators converge to the related minimum Bayes risks with rates of convergence at least of order 0(nsub -n), where n is the number of accumulated past experiences at hand. Further, for the selection problem, the rates of convergence of the proposed selection rules are shown to be at least of order 0(exp( -cn)) for some c> 0. Keywords: Asymptotically optimal.
| Author | : S. S. Gupta |
| Publisher | : |
| Total Pages | : 23 |
| Release | : 1992 |
| Genre | : |
| ISBN | : |
| Author | : Shanti Swarup Gupta |
| Publisher | : |
| Total Pages | : 21 |
| Release | : 1981 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
A problem of selecting populations better than a control is considered. When the populations are uniformly distributed, empirical Bayes rules are derived for a linear loss function for both the known control parameter and the unknown control parameter cases. When the priors are assumed to have bounded supports, empirical Bayes rules for selecting good populations are derived for distributions with truncation parameters (i.e. the form of the pdf is f(x/theta) = Pi(x)ci(theta)I(O, theta)(x)). Monte Carlo studies are carried out which determine the minimum sample sizes needed to make the relative errors less than epsilon for given epsilon-values. (Author).
| Author | : Shanti S. Gupta |
| Publisher | : |
| Total Pages | : 24 |
| Release | : 1985 |
| Genre | : |
| ISBN | : |
This paper deals with the problem of selecting good binomial populations compared with a standard or a control through the empirical Bayes approach. Two cases have been studied: one with the pior distribution completely unknown and the other with the prior distribution symmetrical about p = 1/2, but otherwise unknown. In each case, empirical Bayes rules are derived and their rates of convergence are shown to be of order O(exp( -cn)) for some c>O, where n is the number of accumulated post experiences at hand. Keywords: Statistical decision theory; Smoothing(Mathematics); Asymptotically optimal. (Author).
| Author | : Shanti S. Gupta |
| Publisher | : |
| Total Pages | : 25 |
| Release | : 1983 |
| Genre | : |
| ISBN | : |
This paper deals with the problems of selecting all populations which are close to a control or standard. A general Bayes rule for the above problem is derived. Empirical Bayes rules are derived when the populations are assumed to be uniformly distributed. Under some conditions on the marginal and prior distributions, the rate of convergence of the empirical Bayes risk to the minimum Bayes risk is investigated. (Author).
