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Amitava Mukherjee
Researcher at XLRI- Xavier School of Management
Publications - 96
Citations - 1609
Amitava Mukherjee is an academic researcher from XLRI- Xavier School of Management. The author has contributed to research in topics: Nonparametric statistics & Control chart. The author has an hindex of 20, co-authored 81 publications receiving 1218 citations. Previous affiliations of Amitava Mukherjee include Indian Institute of Technology Madras & Umeå University.
Papers
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A distribution-free control chart for the joint monitoring of location and scale
TL;DR: A single distribution-free Shewhart-type chart is proposed for monitoring the location and the scale parameters of a continuous distribution when both of these parameters are unknown.
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A New Distribution‐free Control Chart for Joint Monitoring of Unknown Location and Scale Parameters of Continuous Distributions
TL;DR: A distribution-free Shewhart-type chart based on the Cucconi statistic is proposed and studied, called the SheWhart-Cucconi (SC) chart, which performs just as well or better as a competing distribution- free chart.
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Distribution-free Phase II CUSUM Control Chart for Joint Monitoring of Location and Scale
TL;DR: The in-control and out-of-control performance properties of the cumulative sum-Lepage (CL) chart are investigated through simulation studies in terms of the average, the standard deviation, the median, and some percentiles of the run length distribution.
Journal Article
Distribution-free exponentially weighted moving average control charts for monitoring unknown location
TL;DR: A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics, is proposed for detecting a shift in the location parameter of a continuous distribution.
Journal ArticleDOI
Distribution-free exponentially weighted moving average control charts for monitoring unknown location
TL;DR: A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics, is proposed in this paper for detecting a shift in the location parameter of a continuous distribution.