• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.445-452
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• 1997

In this paper, twicing technique for the improvement of asymptotic boundary bias in nonparametric regression is considered. Asymptotic mean squared errors of the nonparametric regression estimators are derived at the boundary region by twicing the Nadaraya-Waston and local linear smoothing. Asymptotic biases of the resulting estimators are of order$h^2$and$h^4$ respectively.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.15-27
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• 2001

One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.811-816
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• 2000

The purpose of this paper is to study of data-driven smoothing goodness of it test, when the hypothesis is complete. The smoothing goodness of fit test statistic by nonparametric function estimation techniques is proposed in this paper. The results of simulation studies for he powers of show that the proposed test statistic compared well to other.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.409-418
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• 2006

We propose a nonparametric test procedure for the K-sample problem with grouped data. We construct the test statistics using the scores derived for the linear model based on likelihood ratio principle and obtain asymptotic distribution. Also we illustrate our procedure with an example. Finally we discuss some concluding remarks.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.417-429
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• 1998

This paper compares powers of the two nonparametric tests under a variety of population distributions through a simulation study. Both tests require that the two underlying populations have the same variance, but this assumption is relaxed in some of the comparisons.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.111-119
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• 2007

In this paper, we develop a nonparametric Bayesian methodology of estimating an unknown distribution function F at the given survival time with current status data under the assumption of Dirichlet process prior on F. We compare our algorithm with the nonparametric maximum likelihood estimator through application to simulated data and real data.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.443-457
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• 2007

This paper deals with the problem of testing the existence of change in mean and estimating the change-point using nonparametric bootstrap technique. A test statistic using Gombay and Horvath (1990)'s functional form is applied to derive a test statistic and nonparametric change-point estimator with bootstrapping idea. Achieved significance level of the test is calculated for the proposed test to show the evidence against the null hypothesis. MSE and percentiles of the bootstrap change-point estimators are given to show the distribution of the proposed estimator in simulation.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.781-791
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• 2012

A $2{\times}2$ Cross-over design is widely used in clinical trials for comparison studies of two kinds of drugs or medical treatments. This design has many statistical methods such as Hills-Armitage's (1979) method or Koch's (1972) method. In this paper, we propose a nonparametric test for $2{\times}2$ Cross-over design based on a two-sample test suggested by Baumgartner et al. (1998). In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of previous methods.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.771-771
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• 1999

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.939-946
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• 2008

In this paper the support vector method is presented for the probability density function estimation when the sample observations are contaminated with random noise. The performance of the procedure is compared to kernel density estimates by the simulation study.