Results 141 to 150 of about 163,142 (181)

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.
openaire   +1 more source

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

Logistic Liu Estimator under stochastic linear restrictions

Statistical Papers, 2016
The general logistic regression model is considered. The following stochastic prior information is given $h=H\beta+v$, $E(v)=0$. Here $h$ is a known vector, $H$ is a full rank known matrix, $\beta$ is a vector of regression coefficients to be estimated, and $v$ is a random vector of disturbances with known positive definite variance-covariance matrix ...
Nagarajah Varathan, Pushpakanthie Wijekoon   +1 more
openaire   +1 more source

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.
openaire   +1 more source

Identifying local influential observations in Liu estimator

Metrika, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jahufer, Aboobacker, Chen, Jianbao
openaire   +2 more sources

Liu-type estimator in semiparametric regression models

Journal of Statistical Computation and Simulation, 2010
In this paper, we introduced a Liu-type estimator for the vector of parameters β in a semiparametric regression model. We also obtained the semiparametric restricted Liu-type estimator for the parametric component in a semiparametric regression model. The ideas in the paper are illustrated in a real data example and in a Monte Carlo simulation study.
Akdeniz F., Duran E.A.
openaire   +3 more sources

Particle swarm optimization based Liu-type estimator

Communications in Statistics - Theory and Methods, 2016
In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error,
Inan, Deniz, Egrioglu, Erol, Sarica, Busenur, Askin, Oykum Esra, Tez, Mujgan   +4 more
openaire   +3 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.
openaire   +1 more source

Jackknifed Liu estimator in linear regression models

Wuhan University Journal of Natural Sciences, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Hongchang, Xia, Yuhe
openaire   +2 more sources

Influence Diagnostics in Modified Liu-type Estimator

Calcutta Statistical Association Bulletin, 2016
In regression, it is of interest to detect anomalous observations that exert an unduly large influence on the least squares (LS) analysis. Frequently, the existence of influential data is complicated by the presence of collinearity (see, e.g., Walker and Birch  [1] ).
Hadi Emami, Mostafa Emami
openaire   +1 more source