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Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)

  • 격월간
  • /
  • 1598-9402(pISSN)

Aim & Scope

Journal of the Korean Data & Information Science Society is the official journal of the Korean Data and Information Science Society. It was founded in 1989 and accepts original research articles, short communications, reviews, case studies, and other invited papers in statistics, mathematics, and other related fields where relevant data analysis techniques are applied. The Journal is published bimonthly on January 31, March 31, May 31, July 31, September 30, and November 30. Total or a part of articles in the Journal are abstracted in KCI. The official title of the journal is 'Journal of the Korean Data & Information Science Society'. All submitted manuscripts are peer-reviewed by three reviewers. The text may be written in Korean or English. Tables, figures, and references should be written in English. If the text is written in Korean, English title page should follow the main text of the manuscript. The Journal's circulation is around 450. Manuscripts should be sent to the editorial office (jkdi21c@gmail.com) by email. The URL address of the Society and the Journal is http://www.kdiss.or.kr where full text is available.

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제1권

  1. 장석환 1
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  2. 김광훈 7
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    Bechhofer and Tamhane(1981) developed a theory of optimal incomplete block designs for comparing several treatments with a control. This class of designs is appropriate for comparing simultaneously $p{\geq}2$ test treatments with a control treatment (the so-called multiple comparisons with a control (MCC) problem) when the observations are taken in incomplete blocks of common size $K{\<}p+1$. In this paper we want to extend to partially BTIB designs with two associate classes for the MCC problem. We propose a new class of incomplete block designs that are partially balanced with respect to test treatments. Because the class of designs that we consider is larger than the class of designs in Bechhofer and Tamhane and provides us with designs that improve on the optimal designs in their class. We shall use the abbreviation PBTIB to refer to such designs. We study their structure and give some methods of construction. Also we describe a procedure for making exact joint confidence statements for the MCC problem in PBTIB Designs with two associate classes. We study Optimality, Admissibility considerations in PBTIB designs with two associate classes.
  3. 손중권;김영훈 35
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    The problem of estimating the reliability using the Bayesian approach and the prior information about tile reliability of a lognormal distribution is considered. Some Bayes estimators are proposed and studied under the squared error loss and tile Harris loss. Also Monte Carlo simulations are carried out to examine the performances of the proposed estimators and results are provided in the tables.
  4. 송재기 47
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    A method which determines the number of replications in the simulation is proposed, particularly for small-sample comparison of estimators. This method takes the smallest number of replications that makes the difference of mean square errors be statistically significant and provides an efficient algorithm for calculating the standard error of the mean square error. Two examples are illustrated, the first one is on comparison of mean and median ; the second, the Kaplan-Meier type and Buckley-James type estimators of a quantile function with censored data.
  5. 최영애;송재기 59
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    The Edgeworth expansion, the Roy-Tiku method and the bootstrap method for approximating the distribution of the sample variance are compared through the Monte Carlo simulation study.
  6. 우정수;강석복 67
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    For a two-parameter Pareto distribution, the uniformly minimum variance unbiased estimateors(UMVUE) for the function of the two parameters are expressed in terms of confluent hypergeometric function. The variance of the UMVUE is also expressed in terms of hypergeometric function of several variables. UMVUE's for the ${\gamma}th$ moment about zero and several useful parametric functions, and their variances are obtained as special cases. The estimators of Baxter(1980) and Saksena and Johnson(1984) are special cases of our estimator.
  7. 차영준 77
  8. 최진갑 78