NDLI: Performance of Some Logistic Ridge Regression Estimators

Content Provider	Springer Nature Link
Author	Shukur, Ghazi Månsson, Kristofer Kibria, B. M. Golam
Copyright Year	2011
Abstract	In this paper we generalize different approaches of estimating the ridge parameter k proposed by Muniz et al. (Comput Stat, 2011) to be applicable for logistic ridge regression (LRR). These new methods of estimating the ridge parameter in LRR are evaluated by means of Monte Carlo simulations along with the some other estimators of k that has already been evaluated by Månsson and Shukur (Commun Stat Theory Methods, 2010) together with the traditional maximum likelihood (ML) approach. As a performance criterion we use the mean squared error (MSE). In the simulation study we also calculate the mean value and the standard deviation of k. The average value is interesting firstly in order to see what values of k that are reasonable and secondly if several estimators have equal variance then the estimator that induces the smallest bias should be chosen. The standard deviation is interesting as a performance criteria if several estimators of k have the same MSE, then the most stable estimator (with the lowest standard deviation) should be chosen. The result from the simulation study shows that LRR outperforms ML approach. Furthermore, some of new proposed ridge estimators outperformed those proposed by Månsson and Shukur (Commun Stat Theory Methods, 2010).
Ending Page	414
Page Count	14
Starting Page	401
File Format	PDF
ISSN	09277099
e-ISSN	15729974
Journal	Computational Economics
Issue Number	4
Volume Number	40
Language	English
Publisher	Springer US
Publisher Date	2011-07-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ridge regression Simulation Ridge regression; shrinkage estimators Estimation Multicollinearity Economic Theory Logit MSE
Content Type	Text
Resource Type	Article
Subject	Economics, Econometrics and Finance Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Developing Interaction Shrinkage Parameters for the Liu Estimator — with an Application to the Electricity Retail Market

Performance of Kibria’s methods in partial linear ridge regression model

Difference-based ridge estimator of parameters in partial linear model

Iterative algorithms of biased estimation methods in binary logistic regression

Combining the Liu-type estimator and the principal component regression estimator

An Information Theoretic Approach to Estimation in the Case of Multicollinearity

Efficiency of the modified jackknifed Liu-type estimator

Random Design Analysis of Ridge Regression

Combining two-parameter and principal component regression estimators

Performance of Some Logistic Ridge Regression Estimators

Similar Documents

Developing Interaction Shrinkage Parameters for the Liu Estimator — with an Application to the Electricity Retail Market

Performance of Kibria’s methods in partial linear ridge regression model

Difference-based ridge estimator of parameters in partial linear model

Iterative algorithms of biased estimation methods in binary logistic regression

Combining the Liu-type estimator and the principal component regression estimator

An Information Theoretic Approach to Estimation in the Case of Multicollinearity

Efficiency of the modified jackknifed Liu-type estimator

Random Design Analysis of Ridge Regression

Combining two-parameter and principal component regression estimators

Performance of Some Logistic Ridge Regression Estimators