Estimation of a semiparametric multiplicative density model

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초록

This paper discusses the estimation of a semiparametric structured density model that is very useful in forecasting the density on a region where the data are not observed. The model has many applications in actuarial science and mortality forecasting. It has a multiplicative structure with both parametric and nonparametric components that can be estimated based on the data at hand. The problem of estimating the parametric component is nonstandard since the information about the parametric component can be gathered through the derivative of the nonparametric components that are hard to estimate. We propose a simple procedure of estimating the parametric component, which may be used to construct estimators of the nonparametric components. We show that our estimator is consistent with a certain rate and also that it works quite well for finite sample sizes. (C) 2016 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.

키워드

Semiparametric modelsIn-sample density forecastingBackfittingNewton-Kantorovich TheoremVARYING COEFFICIENT MODELSPERIOD-COHORT MODELCHAIN-LADDER MODELEFFICIENT ESTIMATIONSMOOTHING VARIABLESADDITIVE-MODELS
제목
Estimation of a semiparametric multiplicative density model
저자
Lee, Young Kyung
DOI
10.1016/j.jkss.2016.06.001
발행일
2016-12
유형
Article
저널명
Journal of the Korean Statistical Society
45
4
페이지
647 ~ 653