Constrained local likelihood estimators for semiparametric skew-normal distributions Academic Article

abstract

• A local likelihood estimator for a nonparametric nuisance function is proposed in the context of semiparametric skew-normal distributions. Constraints imposed on such functions result in a nonparametric estimator with a different target function for maximization from classical local likelihood estimators. The optimal asymptotic semiparametric efficiency bound on parameters of interest is achieved by using this estimator in conjunction with an estimating equation formed by summing efficient scores. A generalized profile likelihood approach is also proposed. This method has the advantage of providing a unique estimate in cases where an estimating equation has multiple solutions. Our nonparametric estimator of the nuisance function leads to an estimator of the semiparametric skew-normal density. Both the estimating equation and profile likelihood approaches are applicable to more general skew-symmetric distributions. 2007 Biometrika Trust.

authors

• Hart, Jeffrey

published proceedings

• BIOMETRIKA

author list (cited authors)

• Ma, Y., & Hart, J. D.

citation count

• 16

complete list of authors

• Ma, Yanyuan||Hart, Jeffrey D

publication date

• March 2007

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Efficient Score
• Local Estimator
• Profile Likelihood
• Semiparametric Model

Digital Object Identifier (DOI)

• 10.1093/biomet/asm020

start page

• 119

end page

• 134

volume

• 94

issue

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2Fasm020