On Pure and Mixed Statistical Estimation in Economics

Theil, Henri; Goldberger, Arthur S.

doi:10.2307/2525589

Cited by 499 publications

(175 citation statements)

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“…We propose the Mix Maximum Likelihood Estimator (MME) [11] in logistic regression model which through analogy OME [12] in linear model. Defined as follows ( ) ( )…”

Section: The Proposed Estimatorsmentioning

confidence: 99%

A New Stochastic Restricted Liu Estimator for the Logistic Regression Model

Zuo¹,

Li²

2018

OJS

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In order to overcome the well-known multicollinearity problem, we propose a new Stochastic Restricted Liu Estimator in logistic regression model. In the mean square error matrix sense, the new estimation is compared with the Maximum Likelihood Estimation, Liu Estimator Stochastic Restricted Maximum Likelihood Estimator etc. Finally, a numerical example and a Monte Carlo simulation are given to explain some of the theoretical results.

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“…We propose the Mix Maximum Likelihood Estimator (MME) [11] in logistic regression model which through analogy OME [12] in linear model. Defined as follows ( ) ( )…”

Section: The Proposed Estimatorsmentioning

confidence: 99%

A New Stochastic Restricted Liu Estimator for the Logistic Regression Model

Zuo¹,

Li²

2018

OJS

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“…The Ordinary Least Squares Estimator (OLSE) for the model (1) and the Mixed Estimator (ME) (Theil & Goldberger, 1961) …”

Section: mentioning

confidence: 99%

Principal Component Preliminary Test Estimator in the Linear Regression Model

Arumairajan¹,

Wijekoon²

2016

J. Mod. App. Stat. Meth.

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A Preliminary Test Estimator is introduced based on Principal Component RegressionEstimator defined in the linear regression model when the stochastic restrictions are available in addition to the sample information, and when the explanatory variables are multicollinear. It is further developed as a large sample preliminary test estimator by using Wald (WA), Likelihood Ratio (LR), and Lagrangian Multiplier (LM) tests. Stochastic properties of this estimator based on F test as well as WA, LR, and LM tests are derived, and the performance of the estimator is compared using WA, LR, and LM tests with respect to Mean Square Error Matrix (MSEM). A Monte Carlo simulation is carried out to illustrate the theoretical findings.

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“…Theil and Goldberger (1961) [10] proposed the mixed regression estimator (ME) for the regression model (2.1) with the stochastic restricted prior information (7) ( ) ( )…”

Section: The Proposed Estimator and Its Asymptotic Propertiesmentioning

confidence: 99%