Poisson regression is a popular tool for modeling count data and applied in medical sciences, engineering others. Real data, however, are often over or underdispersed, we cannot apply the regression. To overcome this issue, consider model based on Conway–Maxwell (COMP) distribution. Generally, maximum likelihood estimator used estimation of unknown parameters COMP model. However, existence mult...

The ordinary least square (OLS) estimator is the Best Linear Unbiased Estimator (BLUE) when all linear regression model assumptions are valid. OLS estimator, however, becomes inefficient in presence of multicollinearity. Various one and two-parameter estimators have been proposed to circumvent problem This paper presents a new twoparameter called Liu-Kibria Lukman (LKL) estimator. compared theo...

E quantile sensitivities is important in many optimization applications, from hedging in financial engineering to service-level constraints in inventory control to more general chance constraints in stochastic programming. Recently, Hong (Hong, L. J. 2009. Estimating quantile sensitivities. Oper. Res. 57 118–130) derived a batched infinitesimal perturbation analysis estimator for quantile sensi...

In this paper, under the linear regression model with heteroscedastic and/or correlated errors when the stochastic linear restrictions on the parameter vector are assumed to be held, a generalization of the ordinary mixed estimator (GOME), ordinary ridge regression estimator (GORR) and Generalized least squares estimator (GLSE) is proposed. The performance of this new estimator against GOME, GO...