Browsing by Author "Chakraborti, S."

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Citation - WoS: 107
Citation - Scopus: 119
A Nonparametric Shewhart-Type Signed-Rank Control Chart Based on Runs
(Taylor & Francis Inc, 2007) Chakraborti, S.; Eryılmaz, Serkan
Shewhart-type distribution-free control charts are considered for the known in-control median of a continuous process distribution based on the Wilcoxon signed-rank statistic and some runs type rules. The new charts are more attractive to the practitioner than a basic Shewhart-type signed-rank chart proposed by Bakir ( 2004), as they offer more desirable ( smaller) false alarm rates and ( larger) in-control average run-lengths, and can be easily implemented. In addition to being nonparametric, that is with a known and stable in-control performance for all continuous distributions, a simulation study indicates that the proposed charts can have better out-of-control performance than the Shewhart X-bar chart and the basic signed-rank chart for the normal distribution and for some heavy-tailed distributions such as the double exponential and the Cauchy. A numerical example is provided.
Citation - WoS: 52
Citation - Scopus: 58
A Phase Ii Nonparametric Control Chart Based on Precedence Statistics With Runs-Type Signaling Rules
(Elsevier Science Bv, 2009) Chakraborti, S.; Eryılmaz, Serkan; Human, S. W.
Nonparametric control charts do not require knowledge about the shape of the underlying distribution and can thus be attractive in certain situations. Two new Shewhart-type nonparametric control charts are proposed for monitoring the unknown location parameter of a continuous population in Phase 11 (prospective) applications. The charts are based on control limits given by two specified order statistics from a reference sample, obtained from a Phase I (retrospective) analysis, and using some runs-type signaling rules. The plotting statistic can be any order statistic in a Phase 11 sample; the median is used here for simplicity and robustness. Exact run length distributions of the proposed charts are derived using conditioning and some results from the theory of runs. Tables are provided for practical implementation of the charts for a given in-control average run length (ARL(0)) between 300 and 500. Comparisons of the average run length ARL, the standard deviation of run length (SDRL) and some run length percentiles show that the charts have robust in-control performance and are more efficient when the underlying distribution is t (symmetric with heavier tails than the normal) or gamma (1, 1) (right-skewed). Even for the normal distribution, the new charts are quite competitive. An illustrative numerical example is given. An added advantage of these charts is that they can be applied before all the data are collected which might lead to savings in time and resources in certain applications. (C) 2008 Elsevier B.V. All rights reserved.