• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.19-33
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• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.9-16
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• 1997

The contingent valuation method(CVM) has been used to evaluate the economic value of nonmarket goods such as forest recreation. There are two commonly used CVM questionnaire formats: open-ended and closed-ended. This study evaluates the net economic value associated with day use downhill skiing, using CVM. A random, on-site survey of skiers in Muju, Korea generated the value estimates. In this paper a nonparametric test is introduced to find whether the difference between value estimates from open-ended and closed-ended formats are significantly different because the distributions of WTPs are non-normally distributed. The results show that the net economic benefits of a skier in Muju varies from \15,131 to \25,332. The closed-ended values were 1.15 to 1.67 times as large as the open-ended values, depending on the model specifications. In nonparametric test the mean WTPs of the open-ended and close-ended applications are significantly different. Its reason may be that closed-ended can be more reducing the incentive for strategic behavior than open-ended question. However, we cannot conclude that the closed-ended method is superior to the open-ended method.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.653-664
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• 2000

The problem of inference about the unknown change-point with a change in mean is considered. We suggest a nonparametric change-point estimator using window and prove its consistency when the errors are from the distribution with the mean zero and the common variance. a comparison study is done by simulation on the mean, the variance, and the proportion of matching the true change-points.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.247-255
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• 2003

Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.607-618
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• 2002

Crossover design is often used in clinical trials about chronic diseases like hypertension, asthma and arthritis. In this paper, we suggest nonparametric approaches of Friedman-type rank test based on Bernard-van Elteren test and of aligned method keeping the information of blocks based on the AB/BA/AA/BB crossover design. The simulation results are presented to compare experimental error and power of several methods.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.193-199
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• 2005

A simple method is proposed to detect the number of change points with jump discontinuities in nonparamteric regression functions. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Also, the proposed methodology is suggested as the test statistic for detecting of change points and the direction of jump discontinuities.

• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.46-61
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• 1989

This paper is concerned with the development of a nonparametric procedure for the statistical inference about the nonlinear regression parameters. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both under the null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.435-447
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• 2004

In additive nonparametric regression, Linton and Nielsen (1995) showed that the marginal integration when applied to the local linear smoother produces a rate-optimal estimator of each univariate component function for the case where the dimension of the predictor is two. In this paper we give new formulas for the bias and variance of the marginal integration regression estimators which are valid for boundary areas as well as fixed interior points, and show the local linear marginal integration estimator is in fact rate-optimal when the dimension of the predictor is less than or equal to four. We extend the results to the case of the local polynomial smoother, too.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.111-118
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• 1999

We treat the problem of estimating reliability R(t) = P[Y(t) < X(t)] in the time dependent strength-stress model in which a unit of strength X(t) is subjected to environmental stress Y(t) at time t. In this paper two nonparametric approaches to estimate of R(t) are analyzed and compared with parametric method by simulation.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.