• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.293-303
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• 1999

When test outcomes of units are dichotomous and he infection rate is small group testing is more efficient that noe-to-one testing in estimating the true p and classifying units as infected or not. In this paper two-sided hypothesis testing and confidence intervals are derived based on the UMP(uniformly most powerful) unbiased test. The UMP unbiased approach is compared with Thompson's and Bhattacharyya et al.'s approaches by computing the length of confidence intervals and capture probabilities and shown to have a number of desirable properties. Unequal allocation one of advantages of the proposed approach is also mentioned.

• Journal of the Korean Statistical Society
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• 제18권1호
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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.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.198-212
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• 1995

This study provides a nonparametric procedure for the statistical inference of the parameters in stationary autoregressive processes. 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 underthe 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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• 제25권1호
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• pp.81-94
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• 1996

This paper suggests a new criterion for testing the general linear hypothesis about coefficients in multivariate growth curve model. It is developed from a Bayesian point of view using the highest posterior density region methodology. Likelihood ratio test criterion(LRTC) by Khatri(1966) results as an approximate special case. It is shown that under the simple case of vague prior distribution for the multivariate normal parameters a LRTC-like criterion results; but the degrees of freedom are lower, so the suggested test criterion yields more conservative test than is warranted by the classical LRTC, a result analogous to that of Berger and Sellke(1987). Moreover, more general(non-vague) prior distributions will generate a richer class of tests than were previously available.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.112-121
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• 1995

Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.

• 컴퓨터교육학회논문지
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• 제3권2호
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• pp.107-114
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• 2000

본 연구에서는 통계적 가설검증을 학습자 스스로 실행시켜 보면서 배울 수 있는 통계교육용 프로그램을 개발하였다. 통계적 가설검증은 자료의 분석을 통하여 주장이나 예측을 하고자 하는 학문분야에서 반드시 필요로 하는 개념이며 이의 정확한 적용과 이해가 필수적이다. 그럼에도 많은 사람들이 실제 적용에 있어 어려움을 겪고 있으며 통계학의 오용과 남용의 폐해는 매우 심각하다고 할 수 있다. 따라서 통계학을 쉽고도 올바르게 교육시키는 것은 매우 중요한 문제이다. 이 프로그램은 특히 통계 비전공자들이 좀 더 쉽게 통계적인 개념들을 이해하는 데 도움을 줄 수 있도록 개발되었다. 소리, 동영상과 애니메이션 등을 포함하는 멀티미디어 환경 하에서 구현된 이 프로그램은 단순한 계산결과가 아니라 원리와 과정을 알 수 있도록 구성하였으며 이를 따라 하면서 자연스럽게 통계적 가설검증의 절차와 의미를 습득할 수 있을 것이다.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.217-238
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• 2019

With the advent of so-called "default" Bayesian hypothesis tests, scientists in applied fields have gained access to a powerful and principled method for testing hypotheses. However, such default tests usually come with a compromise, requiring the analyst to accept a one-size-fits-all approach to hypothesis testing. Further, such tests may not have the flexibility to test problems the scientist really cares about. In this tutorial, I demonstrate a flexible approach to generalizing one specific default test (the JZS t-test) (Rouder et al., Psychonomic Bulletin & Review, 16, 225-237, 2009) that is becoming increasingly popular in the social and behavioral sciences. The approach uses two results, the Savage-Dickey density ratio (Dickey and Lientz, 1980) and the technique of encompassing priors (Klugkist et al., Statistica Neerlandica, 59, 57-69, 2005) in combination with MCMC sampling via an easy-to-use probabilistic modeling package for R called Greta. Through a comprehensive mathematical description of the techniques as well as illustrative examples, the reader is presented with a general, flexible workflow that can be extended to solve problems relevant to his or her own work.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.385-396
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• 1998

A computing method of fractional Bayes factor (FBF) for a point null hypothesis is explained. We propose alternative form of FBF that is the product of density ratio and a quantity using the generalized Savage-Dickey density ratio method. When it is difficult to compute the alternative form of FBF analytically, each term of the proposed form can be estimated by MCMC method. Finally, two examples are given.