Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design stage. In practice, however, more competing models may be plausible for the same data. Thus, a possibility is to find an optimal design which take both model discrimination and parameter...

We study the problem of selecting the optimal functional form among a set of non-nested nonlinear mean functions for a semiparametric kernel based regression model. To this end we consider Rissanen's minimum description length (MDL) principle. We prove the consistency of the proposed MDL...

We study the problem of selecting the optimal functional form among a set of non-nested nonlinear mean functions for a semiparametric kernel based regression model. To this end we consider Rissanen's minimum description length (MDL) principle. We prove the consistency of the proposed MDL...

The problem of estimating order-restricted scale parameters of two Gamma distributions is considered under the Pitman closeness criterion. A class of isotonic estimators including the MLE is proposed. Some properties of this class of isotonic estimators is given under the Pitman closeness...

We present a new generalized family of skew two-piece skew-elliptical (GSTPSE) models and derive some its statistical properties. It is shown that the new family of distribution may be written as a mixture of generalized skew elliptical distributions. Also, a new representation theorem for a...

Several univariate proportional reversed hazard models have been proposed in the literature. Recently, Kundu and Gupta (2010) proposed a class of bivariate models with proportional reversed hazard marginals. It is observed that the proposed bivariate proportional reversed hazard models have a...

We propose a new sequential procedure to detect change in the parameters of a process X=(Xt)t∈Z belonging to a large class of causal models (such as AR(∞), ARCH(∞), TARCH(∞), or ARMA–GARCH processes). The procedure is based on a difference between the historical parameter estimator and...