• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.283-292
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• 2008

Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.61-70
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• 2014

A randomized block design is a method to apply a treatment into the experimental unit of each block after dividing into several blocks with a binded homogeneous experimental unit. Jonckheere (1964) and Terpstra (1952), Page (1963), Hollander (1967) proposed various methods of ordered alternative in randomized block design. Especially, Page (1963) test is a weighted combination of within block rank sums for ordered alternatives. In this paper, we suggest a new nonparametric method expanding the Page test for an ordered alternative. A Monte Carlo simulation study is also adapted to compare the power of the proposed methods with previous methods.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1181-1189
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• 2015

The treatment effect in clinical tests depending on dose of the drug; however, it can show a decreasing trend in fixed dose level due to side effects. The trend is known as an umbrella pattern; in addition, the method for the umbrella alternative is quite useful when the tendency is predicted in advance. In this paper, we propose a nonparametric method of umbrella alternatives for a one-way layout by using linear placement described in Orban and Wolfe (1982). The Monte Carlo simulation is adapted to compare the power of proposed procedure with previous methods.

• Environmental and Resource Economics Review
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• v.25 no.1
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• pp.1-25
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• 2016

This study investigated the variability of WTP estimates(i.e. mean or median) with ad hoc assumptions of specific parametric probability distributions(i.e. normal, logistic, lognormal, and exponential distribution) to estimate WTP function using dichotomous choice CV data on mortality risk reduction. From the perspective of policy decision, the variability of these WTP estimates are intolerable in comparison with those of Turnbull nonparametric estimation method which is free from ad hoc distribution assumptions. The Turnbull nonparametric estimation can avoid a kind of misspecification bias due to ad hoc assumption of specific parametric distributions. Furthermore, the WTP estimates by Turnbull nonparametric estimation are robust because the similar estimates are elicited from a dichotomous choice or double dichotomous choice CV data, and the statistically significant WTP estimates can be obtained even though it is not possible by parametric estimation methods. If there are considerable variability among those WTP estimates by parametric estimation methods in condition with no criteria of model adequacy, the mean WTPs from Turnbull nonparametric estimation can be the robust estimates without ad hoc assumptions, which can avoid controversial issues in the perspective of policy decisions.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.32-43
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• 1996

In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring that arises naturally in quality engineering. This development is based on a general missing information principle that enables us to apply, via an iterative scheme, nonparametric regression techniques for complete data to iteratively reconstructed data from a given sample with censored observations. In particular, additive regression models are extended to right-censored data. This nonparametric regression method is applied to a simulated data set and the estimated smooth functions provide insights into the relationship between failure time and explanatory variables in the data.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.161-172
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• 2005

In this paper a nonparametric method is proposed for detecting conditionally heteroscedastic errors in a nonparametric time series regression model where the observation points are equally spaced on [0,1]. It turns out that the first-order sample autocorrelation of the squared residuals from the kernel regression estimates provides essential information. Illustrative simulation study is presented for diverse errors such as ARCH(1), GARCH(1,1) and threshold-ARCH(1) models.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.35-46
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• 1994

We propose the nonparametric rank-like test for the location parameter in the bivariate changepoint model. Empirical powers between the parametric test and nonparametric test are compared. These results show that rank-like test is better than parametric method except bivariate normal null distribution. The point estimators for the changepoint are also compared by the empirical mean squared errors.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1105-1112
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• 2011

Mack (1981), Skilling and Wolfe (1977, 1978) proposed typical nonparametric method in randomized block design with replications. In this paper, we proposed the procedures based on placement as extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment versus control tests described in Kim (1999). Also Monte Carlo simulation study is adapted to compare power of the proposed procedure with those of previous procedures.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.611-622
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• 2014

The inverse Weibull distribution (IWD) is the complementary Weibull distribution and plays an important role in many application areas. In Bayesian analysis, Soland's method can be considered to avoid computational complexities. One limitation of this approach is that parameters of interest are restricted to a finite number of values. This paper introduce nonparametric Bayesian estimator in the context of record statistics values from the exponentiated inverse Weibull distribution (EIWD). In stead of Soland's conjugate piror, stick-breaking prior is considered and the corresponding Bayesian estimators under the squared error loss function (quadratic loss) and LINEX loss function are obtained and compared with other estimators. The results may be of interest especially when only record values are stored.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.236-241
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• 2022

We consider methods of estimating a binary regression function using a nonparametric kernel estimation when there is only one covariate. For this, the Nadaraya-Watson estimation method using single and double bandwidths are used. For choosing a proper smoothing amount, the cross-validation and plug-in methods are compared. In the real data analysis for case study, German credit data and heart disease data are used. We examine whether the nonparametric estimation for binary regression function is successful with the smoothing parameter using the above two approaches, and the performance is compared.