• Clinical and Experimental Reproductive Medicine
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• v.27 no.2
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• pp.191-200
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• 2000

Objective: This study was carried out to compare the effects of the stepwise exposure treatments on the morphological normality, fertilization and blastocyst formation rate of the frozen-thawed mouse mature oocytes by vitrification or ultra-rapid freezing and to use as a fundamental data for the cryopreservation of human oocytes. Materials and Methods: The morphological normality and fertilization rates of the vitrified and ultra-rapid frozen mouse mature oocytes after three-stepwise exposure treatments (1step, 3step and 5step) were observed. After choosing the 3step exposure treatment groups, we observed the morphological normality and fertilization, blastocyst formation rate of the vitrified and ultra-rapid frozen mouse mature oocytes. Results: The morphological normality and fertilization rates of the vitrified mouse mature oocytes after three-stepwise exposure treatments (1step, 3step and 5step) were 75%, 85%, 88% and 58%, 61 %, 54% respectively. There were no significant differences among treatments(p>0.05). The morphological normality and fertilization rate of the control was 92% and 65%. There were no significant differences in fertilization rate among control and treatments (p>0.05). The morphological normality and fertilization rates of the ultra-rapid frozen mouse mature oocytes after three-stepwise exposure treatments (1step, 3step and 5step) were 83%, 83%, 84% and 75%, 63%, 56% respectively. There were no significant differences among treatments (p>0.05). The morphological normality and fertilization rate of the control was 95% and 67%. There were no significant differences among control and treatments (p>0.05). The morphological normality and fertilization rate of the vitrified or ultra-rapid frozen mouse mature oocytes after 3step exposure treatment were 69% and 75%, respectively. The blastocyst formation rate was 60% and 57%. The results did not differ significantly between vitrification and ultra-rapid freezing (p>0.05). Conclusion: As known in the above results, there were no significant differences in the fertilization and blastocyst formation rate of the frozen-thawed mouse mature oocytes by vitrification or ultra-rapid freezing among the control and treatments. It is suggested that vitrification and ultra-rapid freezing method were effective for the cryopreservation of mouse mature oocytes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.18-27
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• 2019

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• International Journal of Reliability and Applications
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• v.4 no.4
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• pp.171-181
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• 2003

This paper proposes a U-test statistic for the problem of testing that a life distribution is exponential against the alternative that it is harmonic new better (worse) than used in expectation upper tail HNBUET (HNWUET), but not exponential on complete data. Selected critical values are tabulated for sample sizes n =5(1)60. The asymptotic normality of the statistic is proved and a comparison is made of the asymptotic efficiency between the statistic and other statistics. The power of the test is studied by simulation. A test for HNBUET in the case of randomly right-censored data is also considered. An application of the proposed test statistic in medical sciences is given.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.385-392
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• 2000

Many tests for multivariate normality are based on the spherical coordinates of the scaled residuals of multivariate observations. Moore and Stubblebine's (1981) Pearson chi-square test is based on the radii of the scaled residuals, or equivalently the sample Mahalanobis distances of the observations from the sample mean vector. The chi-square statistic does not have a limiting chi-square distribution since the unknown parameters are estimated from ungrouped data. We will derive a simple closed form of the Rao-Robson chi-square test statistic and provide a self-contained proof that it has a limiting chi-square distribution. We then provide an illustrative example of application to a real data with a simulation study to show the accuracy in finite sample of the limiting distribution.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.235-245
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• 2000

In this paper, we consider the Regression Quantiles Estimators in nonlinear regression models. This paper provides the sufficient conditions for strong consistency and asymptotic normality of proposed estimation and drives asymptotic relative efficiency of proposed estimatiors with least square estimation. We give some examples and results of Monte Carlo simulation to compare least square and regression quantile estimators.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.223-232
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• 2002

The present paper develops a test of the multivariate normality based on nonlinear transformations and the likelihood function. For checking the normality, we test the shape parameter which indexes the family of transformations. A score test and a parametric bootstrap test are used to evaluate the discrepancy between the data and a multivariate normal distribution. In order to compare the performance of our test with the existing tests, a simulation study was carried out for several situations where nuisance parameters have to be estimated. The results showed that the proposed method is superior to the existing methods.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.121-130
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• 1993

For the One-way random effects model with unbalanced data, the AOV and MINQUE estimates of variance components are frequently found to be negative. The primary objective of present study is placed on the computation of the probability of the main effect variance component, being negative. The probability of negative estimators from AOV and MINQUE can be obtained by theoretical computation under the normality assumption. It is, however, difficult to compute the probability of negative estimates for these estimators under arbitrary distributions, and hence their probabilities of being negative were computed by simulation experiment in this study. It was shown that there was no significant difference between the theoretical probability under normality and calculated probability by simulation experiment, and that probability of negative estimates decreases as sample size, number of levels and the value of increase.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.319-332
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• 1997

The application of multivariate linear rank statistics to data with item nonresponse is considered. Only a modest extension of the complete data techniques is required when the missing data may be thought of as a random sample, and an appropriate modification of the covariances is derived. A proof of the asymptotic multivariate normality is given. A review of some related results in the literature is presented and applications including longitudinal and repeated measures designs are discussed.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.57-65
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• 1999

In a resent paper, Na, Lee and Kim(1998) develop a test statistic for testing whether or not the mean residual life changes its trend based on complete data and show that the new test performs better than previously known tests. In this paper, we extend their test to the randomly censored data. The asymptotic normality of the test statistic is established. Monte Carlo simulations are conducted to compare our test with a previously known test by the power of tests.