[논문]Semiparametric kernel logistic regression with longitudinal data

Shim, Joo-Yong; Seok, Kyung-Ha

doi:10.7465/jkdi.2012.23.2.385

[국내논문] Semiparametric kernel logistic regression with longitudinal data 원문보기

Journal of the Korean Data & Information Science Society = 한국데이터정보과학회지, v.23 no.2, 2012년, pp.385 - 392

Shim, Joo-Yong (Department of Data Science, Inje University) , Seok, Kyung-Ha (Department of Data Science, Inje University)

Abstract ▼ AI-Helper

Logistic regression is a well known binary classification method in the field of statistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of optimal hyperparameters, cross-validation techniques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.

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제안 방법

In this paper, we dealt with estimating semiparametric kernel logistic regression of longitudinal data using IRWLS procedure and obtained GCV function. Through the examples we showed that the proposed procedure derives the satisfying results.

이론/모형

The proposed model is derived by employing the penalized likelihood method based on kernel tricks in Vapnik (1995), Smola and Schölkopf (1998).
In this paper we propose a semiparametric kernel logistic regression model (SKLR) for the binary classification of longitudinal data. The proposed model is derived by employing the penalized likelihood method based on kernel tricks in Vapnik (1995), Smola and Schölkopf (1998).
By minimizing the penalized negative log-likelihood (3.3) we obtain the estimator of parameter vector ã' = (b0, β, α, b)', but not in a explicit form, which leads to use the iterative reweighted least squares (IRWLS) procedure.

참고문헌 (17)

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원문보기 상세보기
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상세보기
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원문보기 상세보기
Smola, A. and Scholkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation

상세보기
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
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