• Journal of the Korean Data and Information Science Society
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• 제25권4호
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• pp.951-959
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• 2014

Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimator for the deconvolution problem. In the case of Gaussian kernels and measurement error, they argued that the weighted kernel density estimator is a competitive estimator over the classical deconvolution kernel estimator. In this paper we consider weighted kernel density estimators when sample observations are contaminated by double exponentially distributed errors. The performance of the weighted kernel density estimators is compared over the classical deconvolution kernel estimator and the kernel density estimator based on the support vector regression method by means of a simulation study. The weighted density estimator with the Gaussian kernel shows numerical instability in practical implementation of optimization function. However the weighted density estimates with the double exponential kernel has very similar patterns to the classical kernel density estimates in the simulations, but the shape is less satisfactory than the classical kernel density estimator with the Gaussian kernel.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1231-1239
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• 2012

An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.

• Journal of the Korean Statistical Society
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• 제31권3호
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• pp.359-368
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• 2002

Nonparametric kernel regression problem is considered for a stationary dependent sequence ｛(Xi, Yj) 1 j $\geq$ 1 ｝. In particular consistency and rates of convergence are discussed, which gives some useful insight for the effect of dependence for stationary $\alpha$-mixing sequences.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1539-1547
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• 2014

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.361-368
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• 1993

In recent years, kernel type estimates are abundant. In this paper, we propose a bandwidth selection method for kernel regression of fixed design based on bootstrap procedure. Mathematical properties of proposed bootstrap-based bandwidth selection method are discussed. Performance of the proposed method for small sample case is compared with that of cross-validation method via a simulation study.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• Journal of the Korean Statistical Society
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• 제17권1호
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• pp.1-8
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• 1988

Kernel estimates of an unknown regression function are studied. Bandwidth selection rule minimizing integrated absolute error loss function is considered. Under some reasonable assumptions, it is shown that the optimal bandwidth is unique and can be computed by using bisection algorithm. Adaptive bandwidth selection rule is proposed.

• 응용통계연구
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• 제6권2호
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• pp.329-339
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• 1993

커널을 이용한 회귀선의 추정은 단지 참 함수의 미분성만을 요구하는 비모수적인 회귀선의 추정방법이다. 유한구간에서 어떤 곡선의 완만한 추정곡선을 커널을 이용하여 추정할 때 추 정량의 전체적인 성능을 감소시키는 바운더리 문제가 발생하게 된다. 본논문에서는 바운더 리 문제를 다룰수 있는 커널을 개발하였다. Gray와 Schcany(1972)의 일반화된 jackknife 방 법을 이용하여 바운더리 커널을 개발하였고 또한 이 바운더리 커널이 이 바운더리 커널과 같은 수렴속도를 갖는 것을 보였다.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.905-912
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• 2009

We propose a doubly penalized kernel machine (DPKM) which uses heteroscedastic location-scale model as basic model and estimates both mean and volatility functions simultaneously by kernel machines. We also present the model selection method which employs the generalized approximate cross validation techniques for choosing the hyperparameters which affect the performance of DPKM. Artificial examples are provided to indicate the usefulness of DPKM for the mean and volatility functions estimation.