Results 1 to 10 of about 19,774 (283)

A robust Liu regression estimator

Communications in Statistics - Simulation and Computation, 2017
The least-squares regression estimator can be very sensitive in the presence of multicollinearity and outliers in the data. We introduce a new robust estimator based on the MM estimator.
Peter Filzmoser, Fatma Sevinç Kurnaz
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Robust Liu-type estimator for regression based on M-estimator

Communications in Statistics - Simulation and Computation, 2015
ABSTRACTThe problem of multicollinearity and outliers in the dataset can strongly distort ordinary least-square estimates and lead to unreliable results. We propose a new Robust Liu-type M-estimator to cope with this combined problem of multicollinearity and outliers in the y-direction.
Hasan Ertas, Selahattin Kaçiranlar, Hüseyin Güler   +2 more
openaire   +2 more sources

On the Restricted Liu Estimator in the Logistic Regression Model

Communications in Statistics - Simulation and Computation, 2014
The logistic regression model is used when the response variables are dichotomous. In the presence of multicollinearity, the variance of the maximum likelihood estimator (MLE) becomes inflated. The Liu estimator for the linear regression model is proposed by Liu to remedy this problem. Urgan and Tez and Mansson et al.
Gülesen Üstündag Siray, Selma Toker, Selahattin Kaçiranlar   +2 more
openaire   +3 more sources

Liu-type estimator for the gamma regression model

Communications in Statistics - Simulation and Computation, 2018
In this paper, we propose a new biased estimator called Liu-type estimator in gamma regression models in the presence of collinearity.
Zakariya Yahya Algamal, Yasin Asar
openaire   +2 more sources

Robust Liu estimator for regression based on an M-estimator

Journal of Applied Statistics, 2000
Consider the regression model y = beta 0 1 + Xbeta + epsilon. Recently, the Liu estimator, which is an alternative biased estimator beta L (d) = (X'X + I) -1 (X'X + dI)beta OLS , where ...
Arslan O., Billor N.
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Evaluation of the predictive performance of the Liu type estimator

Communications in Statistics - Simulation and Computation, 2016
Multiple linear regression models are frequently used in predicting (forecasting) unknown values of the response variable y. In this case, a regression model ability to produce an adequate prediction equation is of prime importance. This paper discusses the predictive performance of the Liu estimator compared to ordinary least squares, as well as to ...
Dawoud I., Kaçiranlar S.
openaire   +3 more sources

Evaluation of the Predictive Performance of the Liu Estimator

Communications in Statistics - Theory and Methods, 2013
Multiple linear regression models are frequently used in predicting (forecasting) unknown values of the response variable y. In this case, a regression model ability to produce an adequate prediction equation is of prime importance. This paper discusses the predictive performance of the Liu estimator compared to ordinary least squares, as well as to ...
Özbey F., Kaçiranlar S.
openaire   +3 more sources

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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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
openaire   +1 more source