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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

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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…

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…

math.ST2017

Liu-type Shrinkage Estimations in Linear Models

Bahadır Yüzbaşı, Yasin Asar, S. Ejaz Ahmed

In this study, we present the preliminary test, Stein-type and positive part Liu estimators in the linear models when the parameter vector is partitioned into two pa…

math.ST2017

Preliminary testing derivatives of a linear unified estimator in the logistic regression model

Yasin Asar, Bahadır Yüzbaşı, Mohammad Arashi +1

Recently, the well known Liu estimator (Liu, 1993) is attracted researcher's attention in regression parameter estimation for an ill conditioned linear model. It is also argued tha…

math.ST2017

On the restricted almost unbiased Liu estimator in the Logistic regression model

Jibo Wu, Yasin Asar, M. Arashi

It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, in the context of biased shr…