A class of almost unbiased and efficient estimators of regression coefficients

Kadiyala, K. Rao

doi:10.1016/0165-1765(84)90178-2

Cited by 43 publications

(17 citation statements)

References 3 publications

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“…A class of almost unbiased estimator was introduced by [28,37,43,44], which considerer the almost unbiased generalized ridge estimator for the ith element of β given bŷ…”

Section: Data and Modelmentioning

confidence: 99%

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

Kalatzis

Bassetto

Azzoni

2011

Journal of Applied Statistics

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This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

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“…A class of almost unbiased estimator was introduced by [28,37,43,44], which considerer the almost unbiased generalized ridge estimator for the ith element of β given bŷ…”

Section: Data and Modelmentioning

confidence: 99%

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

Kalatzis

Bassetto

Azzoni

2011

Journal of Applied Statistics

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“…To tackle this problem, some suitable biased estimators have been developed, such as principal component regression estimator (PCR) [1], ordinary ridge estimator (RE) [2], − class estimator [3], Liu estimator (LE) [4], and − class estimator [5]. Kadiyala [6] introduced a class of almost unbiased shrinkage estimator which can be not only almost unbiased but also more efficient than the LS. Singh et al [7] introduced the almost unbiased generalized ridge estimator by the jackknife procedure, and Akdeniz and Kaçiranlar [8] studied the almost unbiased generalized Liu estimator.…”

Section: Introductionmentioning

confidence: 99%

On the Weighted Mixed Almost Unbiased Ridge Estimator in Stochastic Restricted Linear Regression

Liu

Yang

2013

Journal of Applied Mathematics

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We introduce the weighted mixed almost unbiased ridge estimator (WMAURE) based on the weighted mixed estimator (WME) (Trenkler and Toutenburg 1990) and the almost unbiased ridge estimator (AURE) (Akdeniz and Erol 2003) in linear regression model. We discuss superiorities of the new estimator under the quadratic bias (QB) and the mean square error matrix (MSEM) criteria. Additionally, we give a method about how to obtain the optimal values of parameterskandw. Finally, theoretical results are illustrated by a real data example and a Monte Carlo study.

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“…p are eigenvalues of Z 0 ZÞ is too large then a bias correction procedure is recommended in [5]. If this values are considered to be too small then a bias correction procedure recommended.…”

Section: Introductionmentioning

confidence: 99%

The Examination and Analysis of Residuals for Some Biased Estimators in Linear Regression

Akdeniz

2001

Communications in Statistics - Theory and Methods

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In the presence of collinearity certain biased estimation procedures like ridge regression, generalized inverse estimator, principal component regression, Liu estimator, or improved ridge and Liu estimators are used to improve the ordinary least squares (OLS) estimates in the linear regression model. In this paper new biased estimator (Liu estimator), almost unbiased (improved) Liu estimator and their residuals will be analyzed and compared with OLS residuals in terms of mean-squared error.

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A class of almost unbiased and efficient estimators of regression coefficients

Cited by 43 publications

References 3 publications

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

On the Weighted Mixed Almost Unbiased Ridge Estimator in Stochastic Restricted Linear Regression

The Examination and Analysis of Residuals for Some Biased Estimators in Linear Regression

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