• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.339-347
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• 2006

In mixture experiments, performing experiments in highly constrained regions causes collinearity problems. We can use the ridge regression as a means for stabilizing the coefficient estimators in the fitted model. But there is no theory available on which to base statistical inference of ridge estimators. The bootstrap technique could be used to seek the confidence intervals for ridge estimators.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.53-64
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• 1998

Recently, it is of great interest among engineers and reliability scientists to consider a statistical model to describe the failure times of various types of repairable systems. The main subject we deal with in this paper is the power law process which is proved to be a useful model to describe the reliability growth of the repairable system. In particular, we derive the bootstrap confidence intervals of the mean time between two successive failures of a repairable system using the time truncated data. We also compare our bootstrap confindence intervals with Crow's (1982) confidence interval.

• The Korean Journal of Applied Statistics
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• v.2 no.1
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• pp.18-34
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• 1989

The problem of simultaneously estimating the pairwise differences of means of four independent normal populations with equal variances is considered. A statistical computing procedure involving a trivariate t density constructs the exact confidence intervals with simultaneous co verage probability equal to $1-\alpha$. For equal sample sizes, the new procedure is the same as the Tukey studentized range procedure. With unequal sample sizes, in the sense of efficiency for confidence interval lengths and experimentwise error rates, the procedure is superior to the various generalized Tukey procedures.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.429-443
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• 1997

We consider the problem of obtaining several types of simultaneous confidence bands for the survival curve under the additive risk model. The derivation uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approxomated through simulation. The bands are illustrated by applying them from two well-known clinicla studies. Finally, simulation studies are carried outo to compare the performance of the proposed bands for the survival function under the additive risk model.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.127-135
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• 2001

In this paper, we consider a simple method for testing the assumption of independent censoring on the basis of a Cox proportional hazards regression model with a time-dependent covariate. This method involves a two-stage sampling in which a random subset of censored observations is selected and followed-up until their true survival times are observed. Lee and Wolfe(1998) proposed an adjusted estimate of the survivor function for the dependent censoring under a proportional hazards alternative. This paper extends their result to obtain a bootstrap confidence interval for the adjusted survivor function under the dependent censoring. The proposed procedure is illustrated with an example of a clinical trial for lung cancer analysed in Lee and Wolfe(1998).

• Journal of the Korean Statistical Society
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• v.27 no.3
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• pp.359-368
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• 1998

Let ξ$_{p}$(z$_{0}$) be the pth quantile of the distribution of the survival time of an individual with time-invariant covariate vector z$_{0}$ in the additive risk model. We propose an estimator of (ξ$_{p}$(z$_{0}$) and derive its asymptotic distribution, and then construct an approximate confidence interval of ξ$_{p}$(z$_{0}$) . Simulation studies are carried out to investigate performance of the proposed estimator far practical sample sizes in terms of empirical coverage probabilities. Also, the estimator is illustrated on small cell lung cancer data taken from Ying, Jung, and Wei (1995) .d Wei (1995) .

• Communications for Statistical Applications and Methods
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• v.20 no.5
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• pp.347-356
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• 2013

We propose a sequential method to construct approximate confidence limits for the ratio of two independent sequences of binomial variates with unequal sample sizes. Due to the nonexistence of an unbiased estimator for the ratio, we develop the procedure based on a modified maximum likelihood estimator (MLE). We generalize the results of Cho and Govindarajulu (2008) by defining the sample-ratio when sample sizes are not equal. In addition, we investigate the large-sample properties of the proposed estimator and its finite sample behavior through numerical studies, and we make comparisons from the sample information view points.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.113-121
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• 1990

Let ${X_n : n=1,2,\cdots}$ be iid random variables with distribution $P_{\theta}, \theta \in H$ where $H$ is some abstract parameter space. We consider a sequential confidence interval I for the mean $\mu = \mu(\theta)$ of $P_{\theta}$ satisfying $P_{\theta}(\mu \in I) \geq 1-\alpha$ and $P_{\theta}(\mu-\delta(\mu) \in I) \leq \beta$ for all $\theta \in H$ for any given an imprecision real valued function $\delta(\mu) > 0$ and error probabilities $0 < \alpha, \beta < 1$. A one-sided sequential confidence interval is constructed under some restriction of the family {P_{\theta} : \theta \in H}$ and the imprecision function $\delta$. This is extended to the two-sided cases.