Let X be a random variable from a normal distribution with unknown mean θ and known variance σ2. In many practical situations, θ is known in advance to lie in an interval, say [−m,m], for some m > 0. As the usual estimator of θ, i.e., X under the LINEX loss function is inadmissible, finding some competitors for X becomes worthwhile. The only study in the literature considered the problem of min...

let x be a random variable from a normal distribution with unknown mean θ and known variance σ2. in many practical situations, θ is known in advance to lie in an interval, say [−m,m], for some m > 0. as the usual estimator of θ, i.e., x under the linex loss function is inadmissible, finding some competitors for x becomes worthwhile. the only study in the literature considered the problem of min...

The ordinary least square (OLS) method is very efficient in estimating the regression parameters a linear model under classical assumptions. If contains outliers, performance of OLS estimator becomes imprecise. Multicollinearity another issue that can reduce estimator. This study proposed Robust Jackknife Kibria-Lukman (RJKL) based on M-estimator to deal with multicollinearity and outliers. We ...

Estimating the expected value of a random variable by data-driven methods is one most fundamental problems in statistics. In this study, we present an extension Olivier Catoni’s classical M-estimators empirical mean, which focus on heavy-tailed data imposing more precise inequalities exponential moments estimator. We show that our works behave better than Catoni‘s both practice and theory. The ...