Ridge Estimator in Logistic Regression under Stochastic Linear Restrictions

Varathan, Nagarajah; Wijekoon, Pushpakanthie

doi:10.9734/bjmcs/2016/24585

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…We choose the value one in variance which is common in previous literature (see Table 7. RMLE and RRMLE with p = 8. for example, Varathan and Wijekoon [23,24]). In practice, the choice of restriction and particular stochastic restriction should come from theory along with some testing if this theory holds.…”

Section: Simulation Techniquementioning

confidence: 99%

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A new kind of stochastic restricted biased estimator for logistic regression model

Alheety

Månsson

Kibria

2020

Journal of Applied Statistics

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In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator.

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Section: Simulation Techniquementioning

confidence: 99%

“…Varathan and Wijekoon [23] introduced a new biased estimator which is called stochastic restricted ridge maximum likelihood estimator (SRRMLE), and is defined as:…”

Section: Introductionmentioning

confidence: 99%

A new kind of stochastic restricted biased estimator for logistic regression model

Alheety

Månsson

Kibria

2020

Journal of Applied Statistics

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“…When the restrictions on the parameters are stochastic (third type), Nagarajah and Wijekoon (2015) introduced the new estimator called Stochastic Restricted Maximum Likelihood Estimator (SRMLE), and derived the superiority conditions of SRMLE over the LRE, LLE and RMLE. Also, by introducing the Stochastic Restricted Ridge Maximum Likelihood Estimator (SRRMLE) (Varathan and Wijekoon, 2016a), and the Stochastic Restricted Liu Maximum Likelihood Estimator (SRLMLE) (Varathan and Wijekoon, 2016b), the LRE and LLE estimators were further improved in the presence of stochastic restrictions. When comparing the above estimators, it can be noted that incoperating stochastic linear restrictions to the sample model (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…When comparing the above estimators, it can be noted that incoperating stochastic linear restrictions to the sample model (i.e. the third type) improves the estimators further (Nagarajah and Wijekoon (2015), Varathan and Wijekoon (2016a), Varathan and Wijekoon, (2016b). This information motivated us to propose a new estimator under stochastic linear restrictions by considering to improve the performance of the logistic model.…”

Section: Introductionmentioning

confidence: 99%

Liu-Type logistic estimator under Stochastic Linear Restrictions

Varathan

Wijekoon

2018

Ceylon J. Sci.

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Abstract:To conquer the multicollinearity problem in logistic regression, many alternative estimators have been proposed in the literature when some linear restrictions on the parameter space are available in addition to the sample model. In this paper, we propose a new two parameter Liu-type estimator called Stochastic Restricted Liu-Type Logistic Estimator (SRLTLE) by combining Liu-type estimator with the logistic model in the presence of stochastic linear restrictions. Further, a Monte Carlo simulation study is done to compare the performance of the proposed estimator with some existing estimators in the scalar mean squared error (SMSE) sense, and a numerical example is given to illustrate the theoretical results.

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“…For the stochastic linear restrictions, [16], [17] and [18] discussed the logistic regression model. In this paper, we will discuss the logistic regression model with stochastic linear restrictions.…”

Section: Introductionmentioning

confidence: 99%

On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression model

Wu¹,

Asar²

2018

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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In order to overcome multicollinearity, we propose a stochastic restricted Liu-type maximum likelihood estimator by incorporating Liu-type maximum likelihood estimator (Inan and Erdogan, 2013) to the logistic regression model when the linear restrictions are stochastic. We also discuss the properties of the new estimator. Moreover, we give a method to choose the biasing parameter in the new estimator. Finally, a simulation study is given to show the performance of the new estimator.

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Ridge Estimator in Logistic Regression under Stochastic Linear Restrictions

Cited by 15 publications

References 13 publications

A new kind of stochastic restricted biased estimator for logistic regression model

A new kind of stochastic restricted biased estimator for logistic regression model

Liu-Type logistic estimator under Stochastic Linear Restrictions

On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression model

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