Author
Chi-Hyuck Jun
Other affiliations: Electronics and Telecommunications Research Institute, University of Veterinary and Animal Sciences
Bio: Chi-Hyuck Jun is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Control chart & EWMA chart. The author has an hindex of 35, co-authored 309 publications receiving 7442 citations. Previous affiliations of Chi-Hyuck Jun include Electronics and Telecommunications Research Institute & University of Veterinary and Animal Sciences.
Topics: Control chart, EWMA chart, Acceptance sampling, Chart, X-bar chart
Papers published on a yearly basis
Papers
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TL;DR: Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.
Abstract: This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.
1,629 citations
TL;DR: The nature of the VIP method is explored and it is compared with other methods through computer simulation experiments considering four factors–the proportion of the number of relevant predictor, the magnitude of correlations between predictors, the structure of regression coefficients, andThe magnitude of signal to noise.
1,595 citations
TL;DR: Variables single, and double sampling plans are proposed for the lot acceptance of parts whose life follows a Weibull distribution with known shape parameter, which are different from the existing ones in that the lotaccept criteria do not depend on the estimated scale parameter.
Abstract: Sudden death testing can be utilized for deciding upon the lot acceptance of manufactured parts. Variables single, and double sampling plans are proposed for the lot acceptance of parts whose life follows a Weibull distribution with known shape parameter. The proposed plans are different from the existing ones in that the lot acceptance criteria do not depend on the estimated scale parameter. Design parameters of both sampling plans are determined by using the usual two-point approach. The number of groups is determined independently of the group size, and even independently of the shape parameter. Also, the double sampling plan can reduce the average number of groups required. The effects of mis-specification of the shape parameter on the probability of accepting the lots under the single sampling plan are analyzed & discussed.
166 citations
TL;DR: In this article, a multiple dependent (or deferred) state sampling plan by variables for the inspection of normally distributed quality characteristics is proposed, where the decision upon the acceptance of the lot is based on the states of the preceding lots (dependent state plan) or on the state of the forthcoming lots (deferred state plan).
163 citations
TL;DR: The proposed method introduces predicted future responses in a loss function, which accommodates robustness and quality of predictions as well as bias in a single framework to give more reasonable results than the existing methods.
Abstract: A new method for multiresponse optimization is proposed that accommodates robustness, quality of prediction, and bias into a single framework by including predicted future responses in a loss function. Two examples demonstrate that the method gives more..
157 citations
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Journal Article•
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON
13,333 citations
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
6,278 citations
01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
2,933 citations