Bayesian variable selection in quantile regression models is often a difficult task due to the computational challenges and non-availability of conjugate prior distributions. These challenges are rarely addressed via either penalized likelihood function or stochastic search variable selection....

The explanation of heterogeneity when combining different studies is an important issue in meta analysis. Besides including a heterogeneity parameter in the analysis, it is also important to understand the possible causes of heterogeneity. A possibility is to incorporate study-specific...

Inference procedures based on density based minimum distance techniques provide attractive alternatives to likelihood based methods for the statistician. The minimum disparity estimators are asymptotically efficient under the model; several members of this family also have strong robustness...

Rounding of data is common in practice. The problem of estimating the underlying density function based on data with rounding errors is addressed. A parametric maximum likelihood estimator and a nonparametric bootstrap kernel density estimator are proposed. Simulations indicate that the maximum...

Multivariate control charts are essential tools in multivariate statistical process control. In real applications, when a multivariate process shifts, it occurs in either location or scale. Several methods have been proposed recently to monitor the covariance matrix. Most of these methods deal...

The Maximum Likelihood (ML) and Cross Validation (CV) methods for estimating covariance hyper-parameters are compared, in the context of Kriging with a misspecified covariance structure. A two-step approach is used. First, the case of the estimation of a single variance hyper-parameter is...

A new class of extended Birnbaum–Saunders regression models is introduced. It can be applied to censored data and be used more effectively in survival analysis and fatigue life studies. Maximum likelihood estimation of the model parameters with censored data as well as influence diagnostics...

A heterogeneous thresholds probit for ordered ratings is developed to remove conditional independence among responses and incorporate respondent traits. We propose a semiparametric approach to relaxing normality of random-effects in the probit model that account for differences in response...

Distinguishing among linear and nonlinear time series or between nonlinear time series generated by different underlying processes is challenging, as second-order properties are generally insufficient for the task. Different nonlinear processes have different nonconstant bispectral signatures,...

Random Forests are frequently applied as they achieve a high prediction accuracy and have the ability to identify informative variables. Several approaches for variable selection have been proposed to combine and intensify these qualities. An extensive review of the corresponding literature led...