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Showing 1 to 7 of 7 for “"Bayes estimation"”.
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HIERARCHICAL BAYES ESTIMATION AND EMPIRICAL BEST PREDICTION OF SMALL-AREA PROPORTIONS
… characteristic for small areas using small area estimation (SAE) techniques is a common problem in survey research. The direct survey estimates, usually based on area-specific sample data, are very imprecise or even unavailable due to the small or zero sample sizes in the areas. In order to …
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Complete classes of two-stage estimation procedures for certain finite sample problems
Two-stage Bayes procedures, also known as Bayes double sample procedures, for estimating the mean of exponential family distributions are given by Cohen and Sackrowitz (1984). In their study, they develop double sample Bayes estimation procedures for the mean of exponential family distributions …
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Empirical Bayes procedures in time series regression models
In this dissertation empirical Bayes estimators for the coefficients in time series regression models are presented. Due to the uncontrollability of time series observations, explanatory variables in each stage do not remain unchanged. A generalization of the results of O'Bryan and Susarla is …
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Empirical Bayes via ERM and Rademacher complexities: the Poisson model
We consider the problem of empirical Bayes estimation for (multivariate) Poisson means. Existing solutions that have been shown theoretically optimal for minimizing the regret (excess risk over the Bayesian oracle that knows the prior) have several shortcomings. For example, the classical Robbins …
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Stochastic Models for Triangular Tables with Applications to Cohort Data and Claims Reserving
… Ladder Linear Model" is extended to encompass Bayesian, empirical Bayes and dynamic estimation. The empirical Bayes results are given a credibility theory interpretation, and the advantages and disadvantages of the various approaches are highlighted. Finally, the methods are extended to …
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Statistical models and inference for dynamic networks
… the network. A Markov chain Monte Carlo (MCMC) estimation method within a Bayesian setting is presented. Several useful tools for the researcher arise from this estimation method. First, a method of predicting future relations, or edges, is given. Second, missing data can easily be incorporated …
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Modeling with bivariate geometric distributions
… data using the maximum likelihood estimators, Bayes estimators, and method of moment estimators. The maximum likelihood estimators were obtained by solving score equations. Two methods of moments estimators were compared in each of the several bivariate geometric models using the estimated bias …