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A new Poisson Liu Regression Estimator: method and application
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Statistik.
Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Statistik.ORCID-id: 0000-0002-4535-3630
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Statistik.ORCID-id: 0000-0003-3144-2218
2019 (engelsk)Inngår i: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

This paper considers the estimation of parameters for the Poisson regression model in the presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using the Poisson Liu Regression Estimator (PLRE) and propose some new approaches to estimate this shrinkage parameter. The small sample statistical properties of these estimators are systematically scrutinized using Monte Carlo simulations. To evaluate the performance of these estimators, we assess the Mean Square Errors (MSE) and the Mean Absolute Percentage Errors (MAPE). The simulation results clearly illustrate the benefit of the methods of estimating these types of shrinkage parameters in finite samples. Finally, we illustrate the empirical relevance of our newly proposed methods using an empirically relevant application. Thus, in summary, via simulations of empirically relevant parameter values, and by a standard empirical application, it is clearly demonstrated that our technique exhibits more precise estimators, compared to traditional techniques - at least when multicollinearity exist among the regressors.

sted, utgiver, år, opplag, sider
Taylor & Francis, 2019.
Emneord [en]
MLE; MSE; Poisson regression; Liu estimator; shrinkage estimators; simulation study
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-47296DOI: 10.1080/02664763.2019.1707485ISI: 000504551800001Scopus ID: 2-s2.0-85077385521Lokal ID: ;IHHÖvrigtISOAI: oai:DiVA.org:hj-47296DiVA, id: diva2:1383920
Tilgjengelig fra: 2020-01-09 Laget: 2020-01-09 Sist oppdatert: 2020-01-13

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