Identifiability and bias reduction in the skew-probit model for a binary response

abstract

• 2019, 2019 Informa UK Limited, trading as Taylor & Francis Group. The skew-probit link function is one of the popular choices for modelling the success probability of a binary variable with regard to covariates. This link deviates from the probit link function in terms of a flexible skewness parameter. For this flexible link, the identifiability of the parameters is investigated. Next, to reduce the bias of the maximum likelihood estimator of the skew-probit model we propose to use the penalized likelihood approach. We consider three different penalty functions, and compare them via extensive simulation studies. Based on the simulation results we make some practical recommendations. For the illustration purpose, we analyse a real dataset on heart-disease.

authors

• Sinha, Samiran

published proceedings

• JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION

author list (cited authors)

• Lee, D., & Sinha, S.

citation count

• 7

complete list of authors

• Lee, DongHyuk||Sinha, Samiran

publication date

• June 2019

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION  Journal

keywords

• Bias
• Binary Response
• Bootstrap
• Information Matrix
• Penalized Likelihood
• Skew-probit Link

Digital Object Identifier (DOI)

• 10.1080/00949655.2019.1590579

start page

• 1621

end page

• 1648

volume

• 89

issue

• 9

URL

• http://dx.doi.org/10.1080/00949655.2019.1590579