A model for principal components of correlation matrices is proposed. The model satisfies the correlation constraint (i.e., unit valued diagonal elements) as well as optional constraints on eigenvalues and/or eigenvectors. The model yields simplified principal components that retain both...

The aim of this paper is to introduce a general bootstrap by exchangeable weight random variables for empirical estimators of the semi-Markov kernels and of the conditional transition probabilities for semi-Markov processes with countable state space. Asymptotic properties of these generalized...

In regression models, the classical assumption of normal distribution of the random observational errors is often violated, masking some important features of the variability present in the data. Some practical actions to solve the problem, like transformation of variables to achieve normality,...

Marshall and Olkin (1997)  [14] provided a general method to introduce a parameter into a family of distributions and discussed in details about the exponential and Weibull families. They have also briefly introduced the bivariate extension, although not any properties or inferential issues...

This paper considers the problem of estimating fixed effects, random effects and variance components for the multi-variate random effects model with complete and incomplete data. It also considers making inferences about fixed and random effects, a problem which requires careful consideration of...

We consider a marginal mixture cure model for clustered survival data in which there may be a cure fraction and the survival times may be correlated. We propose a generalized estimating equation approach by incorporating working correlation matrices into an EM algorithm to estimate the...

In this paper, we introduce a unified skew Gaussian-log Gaussian model and propose a general class of spatial sampling models that can account for both heavy tails and skewness. This class includes some models proposed previously in the literature. The likelihood function involves analytically...

Latent variable models represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In such models, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. Numerical...

In this paper, we introduce a matrix variate slash distribution as a scale mixture of the matrix variate normal and the uniform distributions. We study some properties of the proposed distribution and give maximum likelihood (ML) estimators of its parameters using EM algorithm. We provide an...