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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 18, Issue 6 - Nov 2011
Volume 18, Issue 5 - Sep 2011
Volume 18, Issue 4 - Jul 2011
Volume 18, Issue 3 - May 2011
Volume 18, Issue 2 - Mar 2011
Volume 18, Issue 1 - Jan 2011
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Jurisprudence in the History of Statistics
Jo, Jae-Keun ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 559~570
DOI : 10.5351/CKSS.2011.18.5.559
The role of jurisprudence is examined in the early history of probability and statistics. From the mid-17th to the early 18th century, Christiaan Huygens and Jacob Bernoulli used mathematical expectation to solve the problems that originated from games of chance. We demonstrate that their concept of expectation as a fair price for participating in a game came from the legal concept of 'fair trade'. In addition, we consider that the probability that Bernoulli defined in his Ars Conjectandi originated from the legal concept of 'degree of certainty'. After considering some contributions of Laplace and Poisson, we examined the history of census and statistical survey in the early 19th century. Contrary to the history of the 17th and 18th century, statistics influenced society and law in the 19th century.
Bivariate Zero-Inflated Negative Binomial Regression Model with Heterogeneous Dispersions
Kim, Dong-Seok ; Jeong, Seul-Gi ; Lee, Dong-Hee ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 571~579
DOI : 10.5351/CKSS.2011.18.5.571
We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.
Continual Reassessment Method in Phase I Clinical Trials for Leukemia Patients
Lee, Joo-Hyoung ; Song, Hae-Hiang ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 581~594
DOI : 10.5351/CKSS.2011.18.5.581
The traditional method of 3+3 standard design and model-based Bayesian continual reassessment method (CRM) are commonly used in Phase I clinical trials to identify the maximal tolerated dose(MTD) of a new drug. In this paper we review clinical examples of Phase I trials that were carried out in patients with refractory or relapsed leukemia and myelodysplastic syndrome. The recently proposed 3+1+1 design and rolling-6 design can shorten the trial duration, when a very slow accrual of patients with a simple 3+3 standard design may result in the untimely termination of trials. Too conservative approaches in determining the dose levels in Phase I clinical trials can leave clinical investigators unable to accurately determine the MTD. When determining future patient doses, the designs that use a time-to-event CRM can cooperate late toxicities by accounting for the proportion of the observation period of each enrolled patient. With the CRM design, simulations under different scenarios during the trial are important in detecting the under- or over-estimation of the initial estimate of the dose-limiting toxicity rate for each dose level. We present the advantages and drawbacks of the designs used in Phase I clinical trials for leukemia patients.
Random Generation of the Social Network with Several Communities
Huh, Myung-Hoe ; Lee, Yong-Goo ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 595~601
DOI : 10.5351/CKSS.2011.18.5.595
A community of the social network refers to the subset of nodes linked more densely among them than to others. In this study, we propose a Monte-Carlo method for generating random social unipartite and bipartite networks with two or more communities. Proposed random networks can be used to verify the small world phenomenon of the social networks with several communities.
Bayesian Estimations on the Exponentiated Distribution Family with Type-II Right Censoring
Kim, Yong-Ku ; Kang, Suk-Bok ; Seo, Jung-In ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 603~613
DOI : 10.5351/CKSS.2011.18.5.603
Exponentiated distribution has been used in reliability and survival analysis especially when the data is censored. In this paper, we derive Bayesian estimation of the shape parameter, reliability function and failure rate function in the exponentiated distribution family based on Type-II right censored data. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, the mean square errors of the estimates are computed. Comparisons are made between these estimators using Monte Carlo simulation study.
The Influence of Extreme Value in Binomial Confidence Interval
Ryu, Jea-Bok ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 615~623
DOI : 10.5351/CKSS.2011.18.5.615
Several methods are used in interval estimation for binomial proportion; however the coverage probabilities of most confidence intervals depart from the confidence level when the binomial population proportion closes to 0 or 1 due to the extreme value. Vollset (1993), Agresti and Coull (1998), Newcombe (1998), and Brown et al. (2001) suggested methods to adjust the extreme value. This paper discusses the influence of extreme value in a binomial confidence interval through the numerical comparison of 6 confidence intervals.
Comparison of Two Parametric Estimators for the Entropy of the Lognormal Distribution
Choi, Byung-Jin ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 625~636
DOI : 10.5351/CKSS.2011.18.5.625
This paper proposes two parametric entropy estimators, the minimum variance unbiased estimator and the maximum likelihood estimator, for the lognormal distribution for a comparison of the properties of the two estimators. The variances of both estimators are derived. The influence of the bias of the maximum likelihood estimator on estimation is analytically revealed. The distributions of the proposed estimators obtained by the delta approximation method are also presented. Performance comparisons are made with the two estimators. The following observations are made from the results. The MSE efficacy of the minimum variance unbiased estimator appears consistently high and increases rapidly as the sample size and variance, n and
, become simultaneously small. To conclude, the minimum variance unbiased estimator outperforms the maximum likelihood estimator.
Graphical Representation of Partially Ranked Data
Han, Sang-Tae ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 637~644
DOI : 10.5351/CKSS.2011.18.5.637
Partially ranked data refers to the situation in which there are p distinct objects; however each judge specifies only first s (s < p) choices. The group theoretic formulation for partially ranked data analysis was set up by Critchlow (1985). We propose a graphical method for partially ranked data by quantifying objects and judges. In a plot for judges, the interpoint distances can be interpreted as Spearman or Kendall distances between two rankings given by respective judges. Similarly, we also construct a plot for objects with a sensible relationship to the previous plot for judges. This study extends the Han and Huh (1995) quantification method of fully ranked data using Gabriel's (1971) biplot technique for multivariate data matrix.
Inference Based on Generalized Doubly Type-II Hybrid Censored Sample from a Half Logistic Distribution
Lee, Kyeong-Jun ; Park, Chan-Keun ; Cho, Young-Seuk ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 645~655
DOI : 10.5351/CKSS.2011.18.5.645
Chandrasekar et al. (2004) introduced a generalized Type-II hybrid censoring. In this paper, we propose generalized doubly Type-II hybrid censoring. In addition, this paper presents the statistical inference on the scale parameter for the half logistic distribution when samples are generalized doubly Type-II hybrid censoring. The approximate maximum likelihood(AMLE) method is developed to estimate the unknown parameter. The scale parameter is estimated by the AMLE method using two di erent Taylor series expansion types. We compar the AMLEs in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20; 30; 40 and various censored samples. The
is better than
in the sense of the MSE.
Estimation in an Exponentiated Half Logistic Distribution under Progressively Type-II Censoring
Kang, Suk-Bok ; Seo, Jung-In ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 657~666
DOI : 10.5351/CKSS.2011.18.5.657
In this paper, we derive the maximum likelihood estimator(MLE) and some approximate maximum likelihood estimators(AMLEs) of the scale parameter in an exponentiated half logistic distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error(MSE) through a Monte Carlo simulation for various censoring schemes. We also obtain the AMLEs of the reliability function.
Estimation of Coverage Growth Functions
Park, Joong-Yang ; Lee, Gye-Min ; Kim, Seo-Yeong ;
Communications for Statistical Applications and Methods, volume 18, issue 5, 2011, Pages 667~674
DOI : 10.5351/CKSS.2011.18.5.667
A recent trend in software reliability engineering accounts for the coverage growth behavior during testing. The coverage growth function (representing the coverage growth behavior) has become an essential component of software reliability models. Application of a coverage growth function requires the estimation of the coverage growth function. This paper considers the problem of estimating the coverage growth function. The existing maximum likelihood method is reviewed and corrected. A method of minimizing the sum of squares of the standardized prediction error is proposed for situations where the maximum likelihood method is not applicable.