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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.
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An assessment of the effect of using different mappings and Minkowski distances in joint monitoring of the time-between-event processes
TL;DR: In this article, the authors investigated four different mappings to analyse the transformation effect on the joint monitoring schemes for a two-parameter exponentially distributed process and showed that mapping the pivots based on the maximum likelihood estimators to standard normal variables is not optimal.
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Comparisons of some memory‐type control chart for monitoring Weibull‐distributed time between events and some new results
TL;DR: In this article , a Mixed GWMA-CUSUM (MGC) chart was proposed to monitor the decreasing mean shift in Weibull time between event (TBE) processes.
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Two new distribution-free two-sample tests for versatile alternative
TL;DR: In this paper, the authors introduce two alternative distribution-free tests for the combined classical-location-scale and Lehmann alternatives, known as the versatile alternative, and compare them with the classical location scale and the Lehmann alternative.
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A class of percentile modified Lepage-type tests
Amitava Mukherjee,Marco Marozzi +1 more
TL;DR: In this paper, a new class of tests based on the Mahalanobis distance between the percentile modified test statistics for location and scale differences is introduced, and the asymptotic distributions of the test statistics are obtained, and small-sample size behavior of the tests is studied and compared to other tests via Monte Carlo simulations.
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Phase-II monitoring of exponentially distributed process based on Type-II censored data for a possible shift in location–scale
TL;DR: Three EWMA schemes for monitoring exponentially distributed processes based on type-II censored data are introduced and the design parameters, such as control limits of the three schemes, are provided and the performance is evaluated in terms of average run length characteristics.