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Biased Adjusted Poisson Ridge Estimators-Method and Application
- Source :
- Iranian Journal of Science and Technology. Transaction A, Science
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- Månsson and Shukur (Econ Model 28:1475–1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Türkan and Özel (J Appl Stat 43:1892–1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE ($$ \hat{k}_{q4} $$ k ^ q 4 ) performed better than the rest in the presence of high to strong (0.80–0.99) multicollinearity situation.
- Subjects :
- Mean squared error
General Mathematics
Maximum likelihood
General Physics and Astronomy
Regression estimator
Poisson distribution
Modified almost unbiased ridge estimators
01 natural sciences
symbols.namesake
0103 physical sciences
Statistics
Poisson regression
0101 mathematics
Mathematics
010308 nuclear & particles physics
010102 general mathematics
Estimator
Mean square error
General Chemistry
Ridge (differential geometry)
Poisson ridge regression
Multicollinearity
Maximum likelihood estimator
symbols
General Earth and Planetary Sciences
General Agricultural and Biological Sciences
Research Paper
Subjects
Details
- ISSN :
- 23641819 and 10286276
- Volume :
- 44
- Database :
- OpenAIRE
- Journal :
- Iranian Journal of Science and Technology, Transactions A: Science
- Accession number :
- edsair.doi.dedup.....dcd8bd2a08c30d8e31a4f7c6825e96c0