This paper deals with the problem of selecting good negative binomial populations as compared with a standard or a control. The main results are based on the use of the empirical Bayes approach. First we derive the monotone empirical Bayes estimators of the concerned parameters. Based on these estimators, we construct monotone empirical Bayes selection rules. Asymptotic optimality properties of...

A novel relational learning approach that tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naı̈ve Bayes only for post-processing the rule sets, the presented approach employs the naı̈ve Bayes criterion to guide its search directly. The proposed technique is implemented in...

Based on progressively Type-II censored samples, the uniformly minimum variance unbiased (UMVU), Bayes and empirical Bayes estimates for the unknown parameter and the reliability function of the Burr model are derived. The Bayes and empirical Bayes estimates are obtained based on absolute error and logarithmic loss functions. We also present a numerical example and a Monte Carlo simulation stud...

2 Lecture 2: Evaluation of Statistical Procedures I 2 2.1 How to compare δ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Comparing risk function I: Bayes risk . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Bayes theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.4 Bayes risk revisited . . . . . . . . . . . . . . . . . . . . . . . . . ...

Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the ...

The prediction of single-cross hybrids in maize is a promising technique for optimizing the use of financial resources in a breeding program. This study aimed to evaluate Genomic Best Linear Unbiased Predictors models for hybrid prediction and compare them with the Bayesian Ridge Regression, Bayes A, Bayesian LASSO, Bayes C, Bayes B, and Reproducing Kernel Hilbert Spaces Regression models, with...

A b s t r a c t As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naï...

The naïve Bayes classifier has been widely applied to decisionmaking or classification. Because the naïve Bayes classifier prefers to dealing with discrete values, an novel discretization approach is proposed to improve naïve Bayes classifier and enhance decision accuracy in this paper. Based on the statistical information of the naïve Bayes classifier, a distributional index is defined in the ...

This paper considers inference under progressive type II censoring with a compound Rayleigh failure time distribution. The maximum likelihood (ML), and Bayes methods are used for estimating the unknown parameters as well as some lifetime parameters, namely reliability and hazard functions. We obtained Bayes estimators using the conjugate priors for two shape and scale parameters. When the two p...

In this paper, the statistical inference of the unknown parameters of a twoparameter inverse Weibull (IW) distribution based on the progressive Type-II censored sample has been considered. The maximum likelihood estimators cannot be obtained in explicit forms, hence the approximate maximum likelihood estimators are proposed, which are in explicit forms. The Bayes and generalized Bayes estimator...