A ridge logistic estimator

Schaefer, Robert L.; Roi, Larry D.; Wolfe, Robert A.

doi:10.1080/03610928408828664

Cited by 209 publications

(128 citation statements)

References 12 publications

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“…Though the ridge logistic estimator proposed by Schaefer et al [15] can overcome multicollinearity, however, this estimator has big bias. In this paper, we propose a new estimator which can be used not only overcome multicollinearity, but also can reduce the bias of the ridge estimator.…”

Section: Nmentioning

confidence: 99%

“…whereẐ is a vector with ith element equals log(π i )+ yi−πî πi (1−πi) and W = diag(π Schaefer et al [15] use the ridge method to overcome the multicollinearity in logistic regression model and propose a ridge logistic estimator. Mansson and Shukur [13], Kibria et al [12] proposed many methods to estimate the ridge parameter in ridge logistic estimator.…”

Section: Nmentioning

confidence: 99%

“…The ridge logistic estimator (RLE) in the logistic regression model presented by Schaefer et al [15] is denoted as follows:…”

Section: The Almost Unbiased Ridge Logistic Estimatormentioning

confidence: 99%

“…

AbstractSchaefer et al [15] proposed a ridge logistic estimator in logistic regression model. In this paper a new estimator based on the ridge logistic estimator is introduced in logistic regression model and we call it as almost unbiased ridge logistic estimator.

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confidence: 99%

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On almost unbiased ridge logistic estimator for the logistic regression model

Wu¹,

Asar²

2015

HJMS

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Schaefer et al. [15] proposed a ridge logistic estimator in logistic regression model. In this paper a new estimator based on the ridge logistic estimator is introduced in logistic regression model and we call it as almost unbiased ridge logistic estimator. The performance of the new estimator over the ridge logistic estimator and the maximum likelihood estimator in scalar mean squared error criterion is investigated. We also present a numerical example and a simulation study to illustrate the theoretical results.

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Section: Nmentioning

confidence: 99%

Section: Nmentioning

confidence: 99%

“…The ridge logistic estimator (RLE) in the logistic regression model presented by Schaefer et al [15] is denoted as follows:…”

Section: The Almost Unbiased Ridge Logistic Estimatormentioning

confidence: 99%

“…

…”

mentioning

confidence: 99%

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On almost unbiased ridge logistic estimator for the logistic regression model

Wu¹,

Asar²

2015

HJMS

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“…Schaffer et al (1984) [1] proposed Ridge Logistic Regression (RLR). Aguilera et al (2006) [2] proposed Principal Component Logistic Estimator (PCLE).…”

Section: Introductionmentioning

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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Ridge regression and direct marketing scoring models

Malthouse¹

1999

Journal of Interactive Marketing

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The objective of a direct marketing scoring model is to pick a specified number of people to receive a particular offer so that the response to the mailing is maximized. This paper shows how ridge regression can be used to improve the performance of direct marketing scoring models. It reviews the key property of ridge regression-it can produce estimates of the slope coefficients having smaller mean squared error than ordinary least squares models. Next, it shows that ridge regression can be used to reduce the effective number of parameters in a regression model. Thus, ridge regression can be used as an alternative to variable subset selection methods such as stepwise regression to control the biasvariance tradeoff of the estimated values. This means that direct marketers can include more variables in a scoring model without danger of overfitting the data. Ridge regression estimates are compared with stepwise regression on direct marketing data. The empirical results suggest that ridge regression provides a more stable way of moderating the model degrees of freedom than dropping variables.

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A ridge logistic estimator

Cited by 209 publications

References 12 publications

On almost unbiased ridge logistic estimator for the logistic regression model

On almost unbiased ridge logistic estimator for the logistic regression model

A New Stochastic Restricted Liu Estimator for the Logistic Regression Model

Ridge regression and direct marketing scoring models

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