Communications for Statistical Applications and Methods

  • 제28권6호
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  • Pages.627-641
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  • 2021
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Generalized nonlinear percentile regression using asymmetric maximum likelihood estimation

  • Lee, Juhee (Department of Statistics, Kyungpook National University) ;
  • Kim, Young Min (Department of Statistics, Kyungpook National University)
  • 투고 : 2021.06.04
  • 심사 : 2021.10.04
  • 발행 : 2021.11.30
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초록

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.

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과제정보

This research was supported by the Kyungpook National University Development Project Research Fund, 2018.

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