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N. S. Upadhye

Bio: N. S. Upadhye is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Negative binomial distribution & Stein's method. The author has an hindex of 8, co-authored 39 publications receiving 175 citations. Previous affiliations of N. S. Upadhye include Indian Institute of Technology Bombay.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a fractional lower-order covariance method (FLOC) was proposed for estimating the parameters of vector autoregressive moving average process (VARMA) of order p, q such that
Abstract: In this article, we propose the fractional lower-order covariance method (FLOC) for estimating the parameters of vector autoregressive moving average process (VARMA) of order p, q such that $$p, q\ge 1$$ with symmetric stable noise. Further, we show the efficiency, accuracy and simplicity of our methods through Monte Carlo simulation.

1 citations

Journal ArticleDOI
TL;DR: In this article , a hybrid methodology that combines symmetric α-stable autoregressive time series and artificial neural networks is proposed, which is validated through Monte-Carlo simulations.

1 citations

Journal ArticleDOI
TL;DR: In this article, it is shown that the tail behavior of the function of nonnegative random variables can be characterized using deterministic functions satisfying certain properties, and the upper and lower bounds for the tail of product of random variables are given.
Abstract: It is shown that the tail behavior of the function of nonnegative random variables can be characterized using deterministic functions satisfying certain properties. Also, the upper and lower bounds for the tail of product of random variables are given. Applications of these results are given to some of the well-known models in economics and risk theory. AMS Subject Classification: 60E05, 60E15, 62P20, 91B30, 91B40

1 citations

Posted Content
TL;DR: In this article, the authors introduced the Mittag-Leffler Levy process and provided two alternative representations of this process: first, in terms of Laplace transform of the marginal densities and next as a subordinated stochastic process.
Abstract: In this article, we introduce Mittag-Leffler Levy process and provide two alternative representations of this process. First, in terms of Laplace transform of the marginal densities and next as a subordinated stochastic process. Both these representations are useful in analyzing the properties of the process. Since integer order moments for this process are not finite, we obtain fractional order moments. Its density function and corresponding Levy measure density is also obtained. Further, heavy tailed behavior of densities and stochastic self-similarity of the process is demonstrated. Our results generalize and complement the results available on Mittag-Leffler distribution in several directions.

1 citations


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Book
01 Jan 2013
TL;DR: In this paper, the authors consider the distributional properties of Levy processes and propose a potential theory for Levy processes, which is based on the Wiener-Hopf factorization.
Abstract: Preface to the revised edition Remarks on notation 1. Basic examples 2. Characterization and existence 3. Stable processes and their extensions 4. The Levy-Ito decomposition of sample functions 5. Distributional properties of Levy processes 6. Subordination and density transformation 7. Recurrence and transience 8. Potential theory for Levy processes 9. Wiener-Hopf factorizations 10. More distributional properties Supplement Solutions to exercises References and author index Subject index.

1,957 citations

Journal Article
TL;DR: Alho and Spencer as discussed by the authors published a book on statistical and mathematical demography, focusing on mature population models, the particular focus of the new author (see, e.g., Caswell 2000).
Abstract: Here are two books on a topic new to Technometrics: statistical and mathematical demography. The first author of Applied Mathematical Demography wrote the first two editions of this book alone. The second edition was published in 1985. Professor Keyfritz noted in the Preface (p. vii) that at age 90 he had no interest in doing another edition; however, the publisher encouraged him to find a coauthor. The result is an additional focus for the book in the world of biology that makes it much more relevant for the sciences. The book is now part of the publisher’s series on Statistics for Biology and Health. Much of it, of course, focuses on the many aspects of human populations. The new material focuses on mature population models, the particular focus of the new author (see, e.g., Caswell 2000). As one might expect from a book that was originally written in the 1970s, it does not include a lot of information on statistical computing. The new book by Alho and Spencer is focused on putting a better emphasis on statistics in the discipline of demography (Preface, p. vii). It is part of the publisher’s Series in Statistics. The authors are both statisticians, so the focus is on statistics as used for demographic problems. The authors are targeting human applications, so their perspective on science does not extend any further than epidemiology. The book actually strikes a good balance between statistical tools and demographic applications. The authors use the first two chapters to teach statisticians about the concepts of demography. The next four chapters are very similar to the statistics content found in introductory books on survival analysis, such as the recent book by Kleinbaum and Klein (2005), reported by Ziegel (2006). The next three chapters are focused on various aspects of forecasting demographic rates. The book concludes with chapters focusing on three areas of applications: errors in census numbers, financial applications, and small-area estimates.

710 citations

Journal ArticleDOI
TL;DR: This text is a revision of the book by Arnold, Costillo, and Sarabia (1992), but with much more depth than the original, and comprises a lively overview of conditionally speciŽ ed models of the conditional distribution.
Abstract: of the conditional distribution speciŽ cations. Chapters 8 and 10 extend these methods from two to more dimensions. Chapter 9 investigates estimation in conditionally speciŽ ed models. Chapter 11 considers models speciŽ ed by conditioning on events speciŽ ed by one variable exceeding a value rather than equaling a value, and Chapter 12 considers models for extreme-value data. Chapter 13 extends conditional speciŽ cation to Bayesian analysis. Chapter 14 describes the related simultaneous-equation models, and Chapter 15 ties in some additional topics. An appendix describes methods of simulation from conditionally speciŽ ed models. Chapters 1–4, plus Chapters 9 and 13, comprise a lively overview of conditionally speciŽ ed models. The remainder of the text constitutes a detailed catalog of results speciŽ c to different conditional distributions. Although this catalog is certainly of value, the reader desiring a briefer and less detailed introduction to the subject might skip the remainder at Ž rst reading. This text is a revision of the book by Arnold, Costillo, and Sarabia (1992). The current version is of similar breadth, but with much more depth than the original. The text is clearly written and accessible with relatively few mathematical prerequisites. I found surprisingly few typographical errors; the authors are to be congratulated for this. In a few cases, regularity conditions for results are not given in full. Generally, this causes little confusion, although something does appear to be missing in the statement of Aczél’s key theorem (Theorem 1.3). Fortunately, most of the results in the sequel are derived from corollaries to this theorem, and the corollaries are stated more precisely. I noted few gaps in the material covered. The only area that I thought was insufŽ ciently represented was application to Markov chain Monte Carlo. Conditional speciŽ cation is particularly important in Gibbs sampling. I believe that many practitioners would beneŽ t from a discussion of the issues involved in these sampling schemes. Each chapter contains numerous exercises. These exercises appear to be at an appropriate level for a graduate course in statistics, and appear to provide appropriate reinforcement for the material in the preceding chapters.

260 citations