• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.51-59
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• 2010

In the case that the regression function has a discontinuity point in generalized linear model, Huh (2009) estimated the location and jump size using the log-likelihood weighted the one-sided kernel function. In this paper, we consider estimation of the unknown number of the discontinuity points in the regression function. The proposed algorithm is based on testing of the existence of a discontinuity point coming from the asymptotic distribution of the estimated jump size described in Huh (2009). The finite sample performance is illustrated by simulated example.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.1-9
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• 2009

Let us consider that the variance function in regression model has a discontinuity/change point at unknown location. Yu and Jones (2004) proposed the local polynomial fit to estimate the log-variance function which break the positivity of the variance. Using the local polynomial fit, Huh (2008) estimate the discontinuity point of the log-variance function. We propose a test for the existence of a discontinuity point in the log-variance function with the estimated jump size in Huh (2008). The proposed method is based on the asymptotic distribution of the estimated jump size. Numerical works demonstrate the performance of the method.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.87-95
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• 2014

In the regression model, Kang and Huh (2006) studied the estimation of the discontinuous variance function using the Nadaraya-Watson estimator with the squared residuals. The local linear estimator of the log-variance function, which may have the whole real number, was proposed by Huh (2013) based on the kernel weighted local-likelihood of the ${\chi}^2$-distribution. Chen et al. (2009) estimated the continuous variance function using the local linear fit with the log-squared residuals. In this paper, the estimator of the discontinuous log-variance function itself or its derivative using Chen et al. (2009)'s estimator. Numerical works investigate the performances of the estimators with simulated examples.