Results 251 to 260 of about 4,819,578 (314)

Inverse Gaussian Liu-type estimator

Communications in Statistics - Simulation and Computation, 2021
The inverse Gaussian regression (IGR) model parameters are generally estimated using the maximum likelihood (ML) estimation method.
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On the Liu estimator in the beta and Kumaraswamy regression models: A comparative study

Communications in Statistics - Theory and Methods, 2021
Multi-collinearity among regressors and consequently ill-conditioning inflates the mean squared error (MSE) of the maximum likelihood estimator (MLE) of the parameters in a regression model. In recent years, the Liu estimator (LE) has been widely used in
Shima Pirmohammadi, H. Bidram
semanticscholar   +1 more source

Liu-Type Logistic Estimator

Communications in Statistics - Simulation and Computation, 2013
It is known that multicollinearity inflates the variance of the maximum likelihood estimator in logistic regression. Especially, if the primary interest is in the coefficients, the impact of collinearity can be very serious. To deal with collinearity, a ridge estimator was proposed by Schaefer et al. The primary interest of this article is to introduce
Deniz Inan, Birsen E. Erdogan
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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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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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Robust restricted Liu estimator in censored semiparametric linear models

Journal of Statistical Computation and Simulation, 2021
It is not unusual to have outliers and multicollinearity simultaneously in censored semiparametric linear models. In this paper for dealing with multicollinearity and outliers we introduce a family of robust censored Liu and non-Liu type of estimates for
Hadi Emami, Kourosh Dadkhah
semanticscholar   +1 more source

Diagnostic measures for the restricted Liu estimator in linear measurement error models

Journal of Statistical Computation and Simulation, 2022
This article concentrated on the diagnostics measures to identify outlier observations using the restricted Liu estimator (RLE) of the vector of parameters in linear measurement error models (LMEMs).
F. Ghapani, B. Babadi
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COMBINING THE LIU ESTIMATOR AND THE PRINCIPAL COMPONENT REGRESSION ESTIMATOR

Communications in Statistics - Theory and Methods, 2001
In this paper we introduce a class of estimators which includes the ordinary least squares (OLS), the principal components regression (PCR) and the Liu estimator [1]. In particular, we show that our new estimator is superior, in the scalar mean-squared error (mse) sense, to the Liu estimator, to the OLS estimator and to the PCR estimator.
Kaçiranlar S., Sakallioglu S.
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Development of a Robust Generalized Least Squares Liu Estimator to Address Some Basic Assumptions Violations in Linear Regression Model

Asian Journal of Probability and Statistics
The linear regression model's parameters are frequently estimated using the ordinary least squares (OLS) estimator. When certain assumptions are met, the OLS is regarded as the best linear unbiased estimator.
Abdulrasheed Bello Badawaire, K. Ayinde, S. O. Olanrewaju   +2 more
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