• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.167-173
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• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable(IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• Journal of the Korean Statistical Society
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• 제34권4호
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• pp.367-375
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• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable (IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.1-14
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• 2001

This paper discusses choosing the weight function of the Hardle and Mammen statistic in nonparametric goodness-of-fit test for regression curve. For this purpose, we modify the Hardle and Mammen statistic and derive its asymptotic distribution. Some results on the test statistic from the wild bootstrapped sample are also obtained. Through Monte Carlo experiment, we check the validity of these results. Finally, we study the powers of the test and compare with those of the Hardle and Mammen test through the simulation.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.211-224
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• 2003

Good's lambda test, a permutation test used to detect the changes of microorganism composition under two pathological conditions, has been quite popular for studying the micro-flora responsible for periodontal disease. A vast number of different micro-flora in the mouth renders the traditional chi-square test inapplicable. The main purpose of this paper is to evaluate the power of this test so that the sample size can be determined at the design stage. The robustness of this test and its comparison to two other intuitive tests are also presented. It is found that a permutation test based on likelihood ratio is more powerful than the lambda test in our simulated cases.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.149-156
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• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.

• 응용통계연구
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• 제15권2호
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• pp.415-421
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• 2002

통계적분석에서 가장 대표적인 가정이 정규성 가정이므로 데이터의 정규성 검정은 매우 중요하다. 이 논문에서는 정규성 검정을 위해 경제학에서 소득분배의 불균형에 관한 척도로 널리 이용되는 Lorenz curve를 변형한 새로운 플롯과 검정통계량을 제시한다. 그리고 제한한 검정을 W검정 (Shapiro and Wilk (1965)), Lorenz curve를 이용한 TL검정(Kang and Cho (1999))과 몬테칼로 방법을 이용하여 검정력을 비교한다. 제안된 검정이 특별한 대립분포의 경우를 제외하고는 대부분 검정력이 높았다.

• Genomics & Informatics
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• 제10권2호
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• pp.117-122
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• 2012

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

• Communications for Statistical Applications and Methods
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• 제3권1호
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• pp.21-27
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• 1996

A testing procedure is considered to the problem of testing whether there exists a shift in scale at an unknown time point whem a fixed number of observations are drawn successively in time. A test statistic based on squared ranks test for equal variances is suggested and its aymptotic distrbution is dereived. Small sample power comparisons are performed.

• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.493-505
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• 2010

Developing a test for independence of random variables X and Y against the alternative has an important role in statistical inference. Kochar and Gupta (1987) proposed a class of tests in view of Block and Basu (1974) model and compared the powers for sample sizes n = 8, 12. In this paper, we evaluate Kochar and Gupta (1987) class of tests for testing independence against quadrant dependence in absolutely continuous bivariate Farlie-Gambel-Morgenstern distribution, via a simulation study for sample sizes n = 6, 8, 10, 12, 16 and 20. Furthermore, we compare the power of the tests with that proposed by G$\ddot{u}$uven and Kotz (2008) based on the asymptotic distribution of the test statistics.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.159-168
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• 1999

We introduce a new conditional signed-rank test for the tree alternatives comparing several treatments with a control in the randomized block design. We demonstrate its performance by comparing with 3 classes of signed-rank tests proposed by Park et al.(1991) in some general situations. In most cases the proposed procedure is simpler to compute and has better power than others.