• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.523-536
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• 2021

Although there exist numerous measures of association, most of them are lacking in generality in that they do not intend to measure the association between heterogeneous type of random variables. On the other hand, many statistical analyzes dealing with complex data sets require a very sophisticate measure of association. In this note, the p-value of independence tests is utilized to obtain a measure of association. The proposed measure of association have some consistency in measuring association between various types of random variables.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1103-1113
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• 2010

An extensively used approach for family based association test(FBAT) is compared with the sib transmission/disequilibrium test(S-TDT), and in particular the adjusted S-TDT, in which the covariance among related siblings is taken into consideration, can provide a more sensitive test statistic for association. A simulation study comparing the three test statistics demonstrates that the type I error rates of all three tests are larger than the prespecified significance level and the power of the FBAT is lower than those of the other two tests. More detailed studies are required in order to assess the influence of the assumed conditions in FBAT on the efficiency of the test.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.151-167
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• 2003

Although there exist numerous measures of association, most of them are lacking in generality in that they do not intend to measure the association between heterogeneous type of random variables. On the other hand, many statistical analyzes dealing with complex data sets require a very sophisticate measure of association. In this note, the p-value of independence tests is utilized to obtain a measure of association. The proposed measure of association have some consistency in measuring association between various types of random variables.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.933-945
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• 2008

The genetic association test on age of onset trait aims to detect the putative gene by means of linear rank tests for a significant trend of onset distributions with genotypes. However, due to the selective sampling of recruiting subjects with ages less than a pre-specified limit, the genotype groups are subject to substantially different censored distributions and thus this is one reason for the low efficiencies in the linear rank tests. In testing the equality of two survival distributions, log-rank statistic is preferred to the Wilcoxon statistic, when censored observations are nonignorable. Therefore, for more then two groups, we propose a generalized log-rank test for trend as a genetic association test. Monte Carlo studies are conducted to investigate the performances of the test statistics examined in this paper.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.753-761
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• 2019

A test procedure based on a Kendall's τ statistic is proposed for the association of bivariate interval censored data. In particular, a leverage bootstrap technique is applied to replace unknown failure times and a classical adjustment method is applied for treating tied observations. The suggested method shows desirable results in simulation studies. An AIDS dataset is analyzed with the suggested method.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.1085-1096
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• 2009

Population stratification can cause spurious associations between genetic markers and disease locus. In order to handle this population stratification in haplotype-based case-control association studies, we added population indicators as covariates to the haplotype trend regression model proposed by Zaykin et al. (2002). We investigated through simulations how both population stratification and measurement error in the estimation of true population of each individual affect type I error probabilities of the association tests based on both Zaykin et al.'s (2002) model and the proposed model. Based on those results, in the situation that there exists population stratification but there is no error in population classification of each individual, our proposed model does satisfy a type I error probability whereas Zaykin et al.'s (2002) model does not. However, as the measurement error increases, a type I error probability of our model correspondingly becomes larger than a nominal significance level. It implies that as long as uncertainty in the estimation of true population of each individual still remains, it is nearly impossible to avoid false positive in case-control association studies based on haplotypes.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.845-855
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• 2010

Extensively used case-control designs in medical studies can also be powerful and efficient for family association studies as long as an analysis method is developed for the evaluation of association between candidate genes and disease. Traditional Cochran-Armitage trend test is devised for independent subjects data, and to apply this trend test to the biologically related siblings one has to take into account the covariance among related family members in order to maintain the correct type I error rate. We propose a more powerful trend test by introducing weights that reflect the number of affected siblings in families for the evaluation of the association of genetic markers related to the disease. An application of our method to a sample family data, in addition to a small-scale simulation, is presented to compare the weighted and unweighted trend tests.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.1-17
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• 2008

The ordinary least squares regression method of Haseman and Elston(1972) is most widely used in genetic linkage studies for continuous traits of sib pairs. Kruglyak and Lander(1995) suggested a statistic which appears to be a nonparametric counterpart to the Haseman and Elston(1972)'s regression method, but in fact these two methods are quite different. In this paper the relationships between these two methods are described and will be compared by simulation studies. One of the characteristics of the sib-pair linkage study is that the explanatory variable has only three different values and thus dependent variable is heavily replicated in each value of the explanatory variable. We propose a weighted least squares regression method which is more appropriate to this situation and the efficiency of the weighted regression in genetic linkage study was explored with normal and non-normal simulated continuous traits data. Simulation studies demonstrated that the weighted regression is more powerful than other tests.

• Journal of the Korean Geographical Society
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• v.43 no.2
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• pp.253-262
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• 2008

The main objective of this paper is to formulate a generalized procedure to extract the first four moments of univariate spatial association measures for statistical testing under the normality assumption and to evaluate the viability of hypothesis testing based on the normal approximation for each of the spatial association measures. The main results are as follows. First, predicated on the previous works, a generalized procedure under the normality assumption was derived for both global and local measures. When necessary matrices are appropriately defined for each of the measures, the generalized procedure effectively yields not only expectation and variance but skewness and kurtosis. Second, the normal approximation based on the first two moments for the global measures fumed out to be acceptable, while the notion did not appear to hold to the same extent for their local counterparts mainly due to the large magnitude of skewness and kurtosis.

• 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.