activity
20162021
most citedEfficiency of the principal component Liu-type estimator in logistic regression model

2 citations · 7 across the 9 of their papers we have counts for

collaborators

12 papers

stat.ME2021

Liu Estimator in the Multinomial Logistic Regression Model

Yasin Asar, Murat Erişoğlu

This paper considers the Liu estimator in the multinomial logistic regression model. We propose some different estimators of the biasing parameter. The mean square error (MSE) is c…

math.ST2019

Inference for Two Lomax Populations Under Joint Type-II Censoring

Yasin Asar, R. Arabi Belaghi

Lomax distribution has been widely used in economics, business and actuarial sciences. Due to its importance, we consider the statistical inference of this model under joint type-I…

math.ST2019

Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data

Yasin Asar, R. Arabi Belaghi

In this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively type-I hybrid censori…

stat.ME20171 cited

LLASSO: A linear unified LASSO for multicollinear situations

M. Arashi, Y. Asar, B. Yuzbasi

We propose a rescaled LASSO, by premultipying the LASSO with a matrix term, namely linear unified LASSO (LLASSO) for multicollinear situations. Our numerical study has shown that t…

stat.ME20171 cited

On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression

Jibo Wu, Yasin Asar

In order to overcome multicollinearity, we propose a stochastic restricted Liu-type max- imum likelihood estimator by incorporating Liu-type maximum likelihood estimator (Inan and…

math.ST20171 cited

Improved Quantile Regression Estimators when the Errors are Independently and Non-identically Distributed

Bahadır Yüzbaşı, Yasin Asar, Ahmet Demiralp +1

In a classical regression model, it is usually assumed that the explanatory variables are independent of each other and error terms are normally distributed. But when these assumpt…