• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.929-937
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• 2003

Kernel type estimations of discontinuity point at an unknown location in regression function or its derivatives have been developed. It is known that the discontinuity point estimator based on $Gasser-M\ddot{u}ller$ regression estimator with a one-sided kernel function which has a zero value at the point 0 makes a poor asymptotic behavior. Further, the asymptotic variance of $Gasser-M\ddot{u}ller$ regression estimator in the random design case is 1.5 times larger that the one in the corresponding fixed design case, while those two are identical for the local polynomial regression estimator. Although $Gasser-M\ddot{u}ller$ regression estimator with a one-sided kernel function which has a non-zero value at the point 0 for the modification is used, computer simulation show that this phenomenon is also appeared in the discontinuity point estimation.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.709-717
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• 2008

The difference of two one-sided kernel estimators is usually used to detect the location of the discontinuity points of regression function. The large absolute value of the statistic imply discontinuity of regression function, so we may use the difference of two one-sided kernel estimators as the test statistic for testing null hypothesis of a smooth regression function. The problem is, however, we only know the asymptotic distribution of the test statistic under $H_0$ and we hardly expect the good performance of test if we rely solely on the asymptotic distribution for determining the critical points. In this paper, we show that if we adjust the bias of test statistic properly, the asymptotic rules hold for even small sample size situation.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.155-178
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• 2024

Regression discontinuity (RD) design is one of the most widely used methods in causal inference for estimation of treatment effect when the treatment is created by a cutpoint from the covariate of interest. There has been little attention to RD design, although it provides a very useful tool for analysis of treatment effect for censored data. In this paper, we define the causal effect for survival function in RD design when the treatment is assigned deterministically by the covariate of interest. We propose estimators of this causal effect for survival data by using transformation, which leads unbiased estimator of the survival function with local linear regression. Simulation studies show the validity of our approach. We also illustrate our proposed method using the prostate, lung, colorectal and ovarian (PLCO) dataset.

• 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 Statistical Society
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• v.33 no.1
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• pp.59-78
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• 2004

A regression function in generalized linear model may have a discontinuity/change point at unknown location. In order to estimate the location of the discontinuity point and its jump size, the strategy is to use a nonparametric approach based on one-sided kernel weighted local-likelihood functions. Weak convergences of the proposed estimators are established. The finite-sample performances of the proposed estimators with practical aspects are illustrated by simulated examples.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.669-678
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• 2007

An algorithm to detect the number of discontinuity points of the variance function in regression model is proposed. The proposed algorithm is based on the left and right one-sided kernel estimators of the second moment function and test statistics of the existence of a discontinuity point coming from the asymptotic distribution of the estimated jump size. The finite sample performance is illustrated by simulated example.

• Journal of Labour Economics
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• v.39 no.3
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• pp.75-97
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• 2016

This paper estimates the peer effect of how improving the under-performing students can affect the academic performance of other students, focusing on the ratio of students more than proficient. We take advantage of the regression discontinuity design of the policy assignment rule of School-for-Improvement (SFI), which targeted the under-performing schools as an identification strategy. The estimation results from the 2009-2010 NAEA (National Assessment of Educational Achievement) reveal that the decrease of the ratio of under-performing students tends to increase the ratio of excellent or proficient students at around the policy cutoff. The finding turns out to be robust to various falsification tests.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.707-716
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• 2006

When the regression function is discontinuous at a point, the variance function is usually discontinuous at the point. In this case, we had better propose a test for the existence of a discontinuity point with the regression function rather than the variance function. In this paper we consider that the variance function only has a discontinuity point. We propose a nonparametric test for the existence of a discontinuity point with the second moment function since the variance function and the second moment function have the same location and jump size of the discontinuity point. The proposed method is based on the asymptotic distribution of the estimated jump size.

• Journal of dental hygiene science
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• v.15 no.6
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• pp.786-793
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• 2015

The purpose of this study to find out factors that affect intention of career discontinuity among dental hygienists. The data was collected from 300 dental hygienists working at dental clinics. The total of 281 copies were used for analysis. Statistical analysis was performed using PASW Statistics 18.0 at the 5% significance level. T-test, ANOVA, factor analysis, reliability analysis, correlation analysis, hierarchical regression analyses were applied in this analysis. The results were as follows: 1. As for intention of career discontinuity by general characteristics, there were statistically significant difference according to age, duration of employment, children, children age and monthly income (p<0.05). 2. As for intention of career discontinuity by job awareness, there were statistically significant difference according to turnover, work-life, retirement age of dental hygienist, duration of job maintenance and re-employment of dental hygienist (p<0.05). 3. As for hierarchical regression analysis of the factors affecting the intention of career discontinuity, the support from employer had most significant influence on intention of career discontinuity (p<0.05). In order to decrease the career discontinuity among dental hygienists, plans that can increase the work-environment should be implemented by improving support from employer.

• 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.