Regression estimators
Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling
... developed estimators of finite population mean using Mixture Regression estimators using multi-auxiliary variables and attributes in two-phase sampling and investigated its finite sample properties ...
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Mixture Regression Estimators Using Multi Auxiliary Variables and Attributes in Two Phase Sampling
... The Mixture Regression estimators in full information case using multiple auxiliary variables and attributes are more efficient than mean per unit, Regression estimator using one auxilia[r] ...
13
Regression Estimators in Ranked Set, Median Ranked Set and Neoteric Ranked Set Sampling
... introduced regression estimators in median and neoteric ranked set sampling designs which are recently introduced by Al-Omari (2012) and Zamanzade and Al-Omari ...suggested estimators with Al- Omari ...
6
A Comparative Study of Generalized Ratio and Regression Estimators with their classical counterparts
... and regression estimators of Brewer, Horvitz and Thompson and Cassel-Sarandal and Wretman ...and regression estimators along with the mean per unit estimator of equal probability ...
6
Generalized P-phased Regression Estimators with Single and Two Auxiliary Variables
... and regression estimator ...p-phase regression estimators with single and two auxiliary variables for estimating population ...proposed estimators are the generalized p-phase cases of Hanif et ...
25
On the Consistency of a Class of Nonlinear Regression Estimators
... E f x f x . Thus J1 fails. The converse is also immediate. Hence our assumptions reduce to the assumptions of Jennrich (1969) in the case of least squares. For linear models, the consistency of the least trimmed ...
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On Bootstrap Confidence Intervals Associated with Nonparametric Regression Estimators for A Finite Population Total
... total estimators constructed from the Nadaraya–Watson estimator (nonparametric regression estimator) and design–based Horvitz–Thompson estimator and he found that nonparametric regression based ...
6
Some Statistical Properties of Spectral Regression Estimators
... review regression type optimization framework of ...In regression type optimiza- tion frameworks, PCA can be formulated as a ridge regression ...
152
Optimizing Forest Sampling by Using Lagrange Multipliers
... and Regression Estimators In a stratified inventory information on some auxiliary variables is used both to plan the sampling design ...or regression estimators, so that the stan- dard error ...
6
Innovation and market value: A quantile regression analysis
... quantile regression may be preferable to the usual regression methods for a number of ...standard regression estimators are not robust to modest departures from normality, quantile ...
10
Finite sample performance of kernel based regression methods for non parametric additive models under common bandwidth selection criterion
... the estimators, a unified plug-in method is proposed for bandwidth selection in the simulation, which is inspired by the DPI method of Opsomer and Ruppert (1998) and involves a common criterion function for ...
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Estimation of Regression Function for Nonequispaced Samples Based on Warped Wavelets
... wavelet estimators for derivative of regression function exist in this ...of regression function has been ...basis estimators is that they are near optimal in the minimax sense over a large ...
9
Estimation under Multicollinearity: Application of Restricted Liu and Maximum Entropy Estimators to the Portland Cement Dataset
... (MEL) estimators of β : Paris (2001-a, 2001-b) introduced the Maximum Entropy Leuven (MEL) ...MEL estimators exploit the information available in the sample data more efficiently than the OLS does; unlike ...
9
Weighted Type of Quantile Regression and its Application
... The CQR in Zou and Yuan (2008) is robust compared to traditional QR. The CQR they used is a sum of differ- ent quantile regression (QR) [Koenker and Bassett (1978)] at predetermined quantiles, which uses equal ...
5
Performance of Existing Biased Estimators and the Respective Predictors in a Misspecified Linear Regression Model
... DOI: 10.4236/ojs.2017.75062 878 Open Journal of Statistics The rest of this article is organized as follows. The model specification and respective OLSE are written in section 2. In section 3, generalized form to ...
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Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach
... Note that this estimator is the simple average of ordinary least squares estimators ( O ), so it is defined in econometric literature 4 as the simple mean group (SMG) estimator. The SMG estimator is also used by ...
15
Some generalized classes of double sampling regression type estimators using auxiliary information
... of estimators and are preferred to usual linear regression estimator, ratio esti- mator, mean per unit estimator and product estimator in the sense of lesser mean square ...of estimators, there ex- ...
5
Shrinkage and Penalty Estimation Strategies in Some Spatial Models
... The SAR model was first introduced by Whittle (1954) who showed that the least squares estimator of β is inconsistent while Ord (1975) showed that the MLEs for such parameters are consistent estimators. ...
239
Instrumental variable estimation in generalized linear measurement error models
... The problem of finding efficient, consistent estimators of parameters for a general regression function in the presence of covariate measurement error has not been solved. The problem wi[r] ...
115
Estimators of Linear Regression Model and Prediction under Some Assumptions Violation
... these estimators but have also noted that their performances and efficiency depend on the structure of the regressor ...two-stage regression me- thods in the context of autocorrelated error ...these ...
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