This paper describes the Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data. First we consider the Bayesian inference of the unknown parameter under a squared error loss function. Although we have discussed mainly the squared error loss function, any...

The objective of the present paper is to propose a predictive power measure for generalized linear models (GLMs). First, basic predictive power measures for GLMs are compared with respect to some desirable properties. We propose a generalized coefficient of determination for GLMs, which is...

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs...

Generalized iterative scaling (GIS) has become a popular method for getting the maximum likelihood estimates for log-linear models. It is basically a sequence of successive I-projections onto sets of probability vectors with some given linear combinations of probability vectors. However, when a...

Randomized complete block designs are common in agricultural and other experiments. In this manuscript, we derive asymptotic procedures as well as finite approximations, for the analysis of data arising from series of such experiments. We do not assume normality of the data, and the within-block...

Theoretical and simulation analysis is performed to examine whether unobserved heterogeneity independent of the included regressors is really an issue in logit, probit and loglog models with both binary and fractional data. It is found that unobserved heterogeneity has the following effects....

A Bayesian approach with an iterative reweighted least squares is used to incorporate historical control information into quantal bioassays to estimate the dose-response relationship, where the logit of the historical control responses are assumed to have a normal distribution. The parameters...

Factorial K-means analysis (FKM) and Reduced K-means analysis (RKM) are clustering methods that aim at simultaneously achieving a clustering of the objects and a dimension reduction of the variables. Because a comprehensive comparison between FKM and RKM is lacking in the literature so far, a...

The usual Gibbs sampling framework of the Bayesian mixture model is extended to account for binned data. This model involves the addition of a latent variable in the model which represents simulated values from the believed true distribution at each iteration of the algorithm. The technique...

The concept of a flexible region describes an infinite variety of symmetrical shapes to enclose a particular region of interest within a space. In experimental design, the properties of a function on the region of interest are analyzed based on a set of design points. The choice of design points...