Amitava Mukherjee

Bio: 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.

A distribution-free control chart for the joint monitoring of location and scale

Abstract: Traditional statistical process control for variables data often involves the use of a separate mean and a standard deviation chart. Several proposals have been published recently, where a single (combination) chart that is simpler and may have performance advantages, is used. The assumption of normality is crucial for the validity of these charts. In this article, 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. The plotting statistic combines two popular nonparametric test statistics: the Wilcoxon rank sum test for location and the Ansari–Bradley test for scale. Being nonparametric, all in-control properties of the proposed chart remain the same and known for all continuous distributions. Control limits are tabulated for implementation in practice. The in-control and the out-of-control performance properties of the chart are investigated in simulation studies in terms of the mean, the standard deviation, the median, and some percentiles of the run length distribution. The influence of the reference sample size is examined. A numerical example is given for illustration. Summary and conclusions are offered. Copyright © 2011 John Wiley & Sons, Ltd.

A New Distribution‐free Control Chart for Joint Monitoring of Unknown Location and Scale Parameters of Continuous Distributions

Abstract: While the assumption of normality is required for the validity of most of the available control charts for joint monitoring of unknown location and scale parameters, we propose and study a distribution-free Shewhart-type chart based on the Cucconi[1] statistic, called the Shewhart-Cucconi (SC) chart. We also propose a follow-up diagnostic procedure useful to determine the type of shift the process may have undergone when the chart signals an out-of-control process. Control limits for the SC chart are tabulated for some typical nominal in-control (IC) average run length (ARL) values; a large sample approximation to the control limit is provided which can be useful in practice. Performance of the SC chart is examined in a simulation study on the basis of the ARL, the standard deviation, the median and some percentiles of the run length distribution. Detailed comparisons with a competing distribution-free chart, known as the Shewhart-Lepage chart (see Mukherjee and Chakraborti[2]) show that the SC chart performs just as well or better. The effect of estimation of parameters on the IC performance of the SC chart is studied by examining the influence of the size of the reference (Phase-I) sample. A numerical example is given for illustration. Summary and conclusions are offered. Copyright © 2013 John Wiley & Sons, Ltd.

Distribution-free Phase II CUSUM Control Chart for Joint Monitoring of Location and Scale

Abstract: A single distribution-free (nonparametric) Shewhart-type chart on the basis of the Lepage statistic is well known in literature for simultaneously monitoring both the location and the scale parameters of a continuous distribution when both of these parameters are unknown. In the present work, we consider a single distribution-free cumulative sum chart, on the basis of the Lepage statistic, referred to as the cumulative sum-Lepage (CL) chart. The proposed chart is distribution-free (nonparametric), and therefore, the in-control properties of the chart remain invariant and known for all continuous distributions. Control limits are tabulated for implementation of the proposed chart in practice. 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. Detailed comparison with a competing Shewhart-type chart is presented. Several existing cumulative sum charts are also considered in the performance comparison. The proposed CL chart is found to perform very well in the location-scale models. We also examine the effect of the choice of the reference value (k) on the performance of the CL chart. The proposed chart is illustrated with a real data set. Summary and conclusions are presented. Copyright © 2014 John Wiley & Sons, Ltd.

Distribution-free exponentially weighted moving average control charts for monitoring unknown location

Abstract: Distribution-free (nonparametric) control charts provide a robust alternative to a data analyst when there is lack of knowledge about the underlying distribution. A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics (EWMA-EX), is proposed for detecting a shift in the location parameter of a continuous distribution. The nonparametric EWMA chart combines the advantages of a nonparametric control chart (known and robust in-control performance) with the better shift detection properties of an EWMA chart. Guidance and recommendations are provided for practical implementation of the chart along with illustrative examples. A performance comparison is made with the traditional (normal theory) EWMA chart for subgroup averages and a recently proposed nonparametric EWMA chart based on the Wilcoxon-Mann-Whitney statistics. A summary and some concluding remarks are given.

Convergence of Probability Measures

Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of probability measures

Abstract: The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Statistical Quality Control

Abstract: The modeling of extreme values is important to scientists in such Ž elds as hydrology, civil engineering, environmental science, oceanography and Ž nance. Stuart Coles’s book on the modeling of extreme values provides an introductory text on the topic. It is a modeling-oriented text with an emphasis on different types of data and analytical approaches. The book is laid out in nine chapters. Following introductory material and discussion of necessary theoretical background are chapters on approaches to extreme values that focus on the different types of data that might be used in an extreme value analysis. These include models for block maximums, threshold models, models for data from stationary and nonstationary processes, and approaches based on point processes. A chapter covers analysis of multivariate extremes, and the Ž nal chapter brie y covers such topics as Bayesian inference, Markov chains, and spatial extremes. Although this is not a data-driven text, it does contain numerous examples and analyses. These examples are used to illustrate the methodology; I would have preferred to see more motivation and interpretation of the results of the analyses. Datasets and S-PLUS programs for the analyses in the text are available at a website. These are easy to use for those slightly familiar with S-PLUS. The appendix describes the programs and illustrates how to access data and use the programs. It also gives links to sites that provide other software. The text does not include problem sets; these would have been useful, especially if the text is to be used in coursework. The text by Reiss and Thomas (2001) contains more thoroughly analyzed datasets, although it is twice the length and not as streamlined as the text under review. The book is meant for individuals with moderate statistical background. Those with coursework in maximum likelihood methods should have no difŽ culty reading and comprehending the text. Overall, this is a good text for someone getting started in extreme value methods.

Stochastic Ageing and Dependence for Reliability

Abstract: (2007). Stochastic Ageing and Dependence for Reliability. Technometrics: Vol. 49, No. 2, pp. 222-222.