• Clinical and Experimental Pediatrics
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• v.51 no.3
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• pp.262-266
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• 2008

Purpose : Glutathione S-transferase (GST) is a polymorphic supergene family of detoxification enzymes that are involved in the metabolism of numerous diseases. Several allelic variants of GSTs show impaired enzyme activity and are suspected to increase the susceptibility to diseases. Bilirubin is bound efficiently by GST members. The most commonly expressed gene in the liver is GSTM1, and GSTT1 is expressed predominantly in the liver and kidneys. To ascertain the relationship between GST and neonatal hyperbilirubinemia, the distribution of the polymorphisms of GSTT1 and GSTM1 were investigated in this study. Methods : Genomic DNA was isolated from 88 patients and 186 healthy controls. The genotypes were analyzed by polymerase chain reaction (PCR). Results : The overall frequency of the GSTM1 null was lower in patients compared to controls (P=0.0187, Odds ratio (OR) =0.52, 95% confidence interval (CI), 0.31-0.88). Also, the GSTT1 null was lower in patients compared to controls (P=0.0014, OR=0.41, 95% CI=0.24-0.70). Moreover, the frequency of the null type of both, in the combination of GSTM1 and GSTT1, was significantly reduced in jaundiced patients (P=0.0008, OR=0.31, 95% CI=0.17-0.61). Conclusion : We hypothesized that GSTM1 and GSTT1 might be associated with neonatal hyperbilirubinemia. However, the GSTT1 and GSTM1 null type was reduced in patients. Therefore the null GSTT1, null GSTM1, and null type of both in the combination of GSTM1 and GSTT1 may be not a risk factor of neonatal jaundice.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.187-196
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• 2002

Testing for normality has always been a center of practical and theoretical interest in statistical research. In this paper a test statistic for bivariate normality is proposed. The underlying idea is to investigate all the possible linear combinations that reduce to the standard normal distribution under the null hypothesis and compare the order statistics of them with the theoretical normal quantiles. The suggested statistic is invariant with respect to nonsingular matrix multiplication and vector addition. We show that the limit distribution of an approximation to the suggested statistic is represented as the supremum over an index set of the integral of a suitable Gaussian Process. We also simulate the null distribution of the statistic and give some critical values of the distribution and power results.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.237-249
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• 1999

It has been issued that the irreconcilability of the classical test for a point null and standard Bayesian formulation for testing such a point null. The infimum of the posterior probability of the null hypothesis is used as measure of evidence against the null hypothesis in Bayesian approach; here the infimum is over the family of priors on the alternative hypotheses which includes all density that are a priori reasonable. For iid observations from a multivariate normal distribution in $\textit{p}$ dimensions with an unknown mean and a covariance matrix propotional to the Identity we consider the difference and the Wolfowitz distance of the distributions of the P-value and the lower bound of the posterior probability over the family of all normal priors. The Wolfowitz distance is interpreted as the average difference of the quantiles of the two distrbutions.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.349-365
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• 1994

We develop in this paper the likelihood ratio test (LRT) for testing $H_1 : F_1 \preceq F_2$ against $H_2 - H_1$ where $H_2$ imposes no restriction on $F_1$ and $F_2$ and '$\preceq$' means failure rate ordering. Both one and two-sample problems will be considered. In the one-sample case, one of the two distributions is known, while we assume in the other case both are unknown. We derive the asymptotic null distribution of the LRT statistic which will be of chi-bar-square type. The main issue here is to determine the least favorable distribution which is stochastically largest within the class of null distributions.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.125-143
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• 1993

The primary objective of the paper is to review the development of approximations of the null distribution of a scan statistic and to show how these approximations were improved. Let $X_1, \cdots, X_N$ be a sequence of independent uniform random variables on an interval (0, t]. A can statistic is defined to be the maximum number of observations in a subinterval of length t $\leq$ T, when we continuously (or discretely) move the subinterval from 0 to T. A scan statistic is used to test whether certain events occur in a cluster aganist a null hypothesis of the uniformity. It is difficult to calculate the exact null distribution of a scan statistic. Several authors have suggested approximations of the null distribution of a scan statistic since Naus(1966). We conceive that a scan statistic can be used for detecting a "hot region" is defined to be a region at which the frequencies of mutations are relatively high. A "hot region" may be regarded as a generalized version of a hot spot. We leave it for a further study the concrete formulation of deteciton a "hot region" in a mutational spectrum.uot; in a mutational spectrum.

• International Journal of Reliability and Applications
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• v.6 no.1
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• pp.53-64
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• 2005

In this paper, we propose a nonparametric test procedure for the multivariate, grouped and right censored data for two sample problem. For the construction of the test statistic, we use the linear rank statistics for each component and apply the permutation principle for obtaining the null distribution. For the large sample case, the asymptotic distribution is derived under the null hypothesis with the additional assumption that two censoring distributions are also equal. Finally, we illustrate our procedure with an example and discuss some concluding remarks. In appendices, we derive the expression of the covariance matrix and prove the asymptotic distribution.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.267-276
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• 2012

Bagai et al. (1989) proposed a distribution-free test for stochastic ordering in the competing risk model, and recently Murakami (2009) utilized a standard saddlepoint approximation to provide tail probabilities for the Bagai statistic under finite sample sizes. In the present paper, we consider the Gaussian-polynomial approximation proposed in Ha and Provost (2007) and compare it to the saddlepoint approximation in terms of approximating the percentiles of the Bagai statistic. We make numerical comparisons of these approximations for moderate sample sizes as was done in Murakami (2009). From the numerical results, it was observed that the Gaussianpolynomial approximation provides comparable or greater accuracy in the tail probabilities than the saddlepoint approximation. Unlike saddlepoint approximation, the Gaussian-polynomial approximation provides a simple explicit representation of the approximated density function. We also discuss the details of computations.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.303-323
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• 1994

In this paper, distribution of Votaw's $\lambda_1$(mvc) criterion has been obtained using inverse Mellin transform, residue theorem and properties of special functions.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.1
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• pp.99-103
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• 1975

A procedure for the test of independence of the observations and the null distribution are studied for a waiting-time distribution of the number of Bernoulli trials required to obtain a preassigned number of successes under Markov dependence. Selected critical values for the test statistic are tabulated.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1039-1055
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• 2014

The distribution of a test statistic under a null hypothesis depends on the unknown distribution of the data and thus is unknown as well. Conditional tests replace the unknown null distribution by the conditional null distribution, that is, the distribution of the test statistic given the observed data. This approach is known as permutation tests and was developed by Fisher (Fisher, 1935). Theoretical framework for permutation tests was given by Strasser and Weber(1999). The coin package developed by Hothon et al. (2006, 2008) implements a unified approach for conditional inference via the generic independence test. Because convenient functions for the most prominent problems are available, users will not have to use the extremely flexible procedure. In this article we briefly review the underlying theory from Strasser and Weber (1999) and explain how to transform the data to perform the generic function independence test. Finally it was illustrated with a few real data sets.