We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. derive estimators parameters using modified maximum likelihood methodology and explore properties so obtained. show that proposed are more efficient robust than commonly used least square estimators. also develop relevant test hypothesis...

We introduce an approximation to the Gaussian copula likelihood of Song, Li, and Yuan (2009, Biometrics 65, 60-68) used to estimate regression parameters from correlated discrete or mixed bivariate or trivariate outcomes. Our approximation allows estimation of parameters from response vectors of length much larger than three, and is asymptotically equivalent to the Gaussian copula likelihood. W...

Consider the linear regression model y = X + u in the usual notation. In many applications the design matrix X is frequently subject to severe multicollinearity. In this paper an alternative estimation methodology, maximum entropy is given and used to estimate the parameters in a linear regression model when the basic data are ill-conditioned. We described the generalized maximum entropy (GME) ...

in this paper, we obtain  bayesian prediction intervals as well as bayes predictive estimatorsunder square error loss for generalized order statistics when the distribution of the underlying population belongs to a family which includes several important distributions.

In this paper, we consider admissible estimation of the parameter ?r in the gamma distribution with truncated parameter space under entropy loss function. We obtain the classes of admissible estimators. The result can be applied to estimation of parameters in the normal, lognormal, pareto, generalized gamma, generalized Laplace and other distributions.

"Estimation of the value of a regression function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique by a generalized jackknife estimator are presented. It is shown that the generalized jackknife technique produces estimators with faster bias rates. In a small example it is investigated, if the generalized jackknife method works for all choices...

 Minimax estimation problems with restricted parameter space reached increasing interest within the last two decades Some authors derived minimax and admissible estimators of bounded parameters under squared error loss and scale invariant squared error loss In some truncated estimation problems the most natural estimator to be considered is the truncated version of a classic...

Standard methods frequently produce zero estimates of dispersion parameters in the underlying linear mixed model. As a consequence, the EBLUP estimate of a small area mean reduces to a simple regression estimate. In this paper, we consider a class of generalized maximum residual likelihood estimators that covers the well-known profile maximum likelihood and the residual maximum likelihood estim...