For the two-parameter exponential family, a linear Bayes method is proposed to simultaneously estimate the parameter vector consisting of location and scale parameters. The superiority of the proposed linear Bayes estimator (LBE) over the classical UMVUE is established in terms of the mean...

We consider the augmented Lagrangian method (ALM) as a solver for the fused lasso signal approximator (FLSA) problem. The ALM is a dual method in which squares of the constraint functions are added as penalties to the Lagrangian. In order to apply this method to FLSA, two types of auxiliary...

Generalized varying coefficient partially linear models are a flexible class of semiparametric models that deal with data with different types of responses. In this paper, we focus on polynomial spline estimator as a computationally easier alternative to the more commonly used local polynomial...

Delineating the root-water-uptake (RWU) under the salinity stress condition accurately is important for making rational saline water irrigation schedules. However, RWU is very complex and still incompletely understood, especially under the salinity stress condition. In this study, the linear...

This article considers the prediction problem of the life-span of a system whose components connected in series and the lifetime of the components follows the exponential distribution with probability density f(x;θ)=θ−1exp⁡(−x/θ)I(x0). Employing the Bayes method, a prior distribution...

This paper deals with the estimation problem in a system of two seemingly unrelated regression equations where the regression parameter is distributed according to the normal prior distribution . Resorting to the covariance adjustment technique, we obtain the best Bayes estimator of the...

For an integer p [greater-or-equal, slanted] 0, Singh has considered a class of kernel estimators [latin small letter f with hook]~(p) of the pth order derivative [latin small letter f with hook](p) of a density [latin small letter f with hook] and showed how specializations of some of the...