Results 251 to 260 of about 2,495,910 (283)

Bootstrap Liu estimators for Poisson regression model

Communications in Statistics - Simulation and Computation, 2021
The Liu estimator is used to get precise estimatesby introducing bootstrap technique to reduce the problem of multicollinearity in Poisson regression model.
Ismat Perveen, Muhammad Suhail
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Detecting influential observations in Liu and modified Liu estimators

Journal of Applied Statistics, 2013
In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models.
Ertas H., Erisoglu M., Kaciranlar S.
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Weighted ridge and Liu estimators for linear regression model

Concurrency and Computation: Practice and Experience, 2022
SummaryIn linear regression model, ridge regression and two‐parameter Liu estimator (LE) are the most widely used methods in recent decade to overcome the problem of multicollinearity especially for ill conditioned cases. In this article, we propose new weighted ridge and Liu estimators which remain positive for each level of multicollinearity and also
Iqra Babar, Sohail Chand
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A new Liu-type estimator for the gamma regression model

Communications in Statistics - Simulation and Computation, 2023
In many real-world problems, there are situations where the dependent variable may have a Gamma distribution. The Gamma Regression Models (GRMs) are preferred when the response variable assumes a Gamma distribution with a given set of independent ...
Esra Ertan, Ali Erkoç, Kadri Ulas Akay
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Liu-type estimator in Conway–Maxwell–Poisson regression model: theory, simulation and application

Statistics
Recently, many authors have been motivated to propose a new regression estimator in the case of multicollinearity. The most well-known of these estimators are ridge, Liu and Liu-type estimators.
Caner Tanis, Yasin Asar
exaly   +2 more sources

A new adjusted Liu estimator for the Poisson regression model

Concurrency and Computation: Practice and Experience, 2021
SummaryThe Poisson regression model (PRM) is usually applied in the situations when the dependent variable is in the form of count data. For estimating the unknown parameters of the PRM, maximum likelihood estimator (MLE) is commonly used. However, its performance is suspected when the regressors are multicollinear.
Muhammad Amin, Muhammad Nauman Akram, B. M. Golam Kibria   +2 more
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Liu and Ridge Estimators-A Comparison

Communications in Statistics - Theory and Methods, 2012
Liu (1993) proposed an estimator that is similar in form but different from the ridge regression estimator of Hoerl and Kennard. More recently, Ozkale and Kaciranlar (2007) proposed a two-parameter variation of the Liu estimator. This new estimator has a number of interesting properties.
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Two Stages Liu Regression Estimator

Communications in Statistics - Simulation and Computation, 2015
This paper introduces a new estimator for multicollinearity and autocorrelated errors. We propose the Two Stages Liu estimator (TL) for the multiple linear regression model which suffers from autocorrelation AR(1) and multicollinearity problems. We use a mixed method to apply the two stages least squares procedure (TS) for deriving the TL estimator. We
Issam Dawoud, Selahattin Kaçiranlar
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A state estimation of Liu equations

AIP Conference Proceedings, 2015
This paper is concerned with state estimation problems for so-called Liu equations. These equations are counterparts of well-known Ito ones and they were introduced by B. Liu under elaboration of his uncertain theory. The Liu equations may be solved backward and they represent a more convenient object for the state estimation problem solution ...
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On the Principal Component Liu-type Estimator in Linear Regression

Communications in Statistics - Simulation and Computation, 2014
In this article, we present a principal component Liu-type estimator (LTE) by combining the principal component regression (PCR) and LTE to deal with the multicollinearity problem. The superiority of the new estimator over the PCR estimator, the ordinary least squares estimator (OLSE) and the LTE are studied under the mean squared error matrix.
Jibo Wu, Hu Yang 0001
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