Nonparametric estimation and variate generation for a nonhomogeneous Poisson process from event count data.(TECHNICAL NOTE): An article from: IIE Transactions
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Author(s)Lawrence M. Leemis
ISBN / ASINB0009GO8B2
ISBN-13978B0009GO8B7
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This digital document is an article from IIE Transactions, published by Institute of Industrial Engineers, Inc. (IIE) on December 1, 2004. The length of the article is 3403 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: Given a finite time horizon that has been partitioned into subintervals over which event counts have been accumulated for multiple realizations of a population NonHomogeneous Poisson Process (NHPP), this paper develops point and confidence-interval estimators for the cumulative intensity (or mean value) function of the population process evaluated at each subinterval endpoint. As the number of realizations tends to infinity, each point estimator is strongly consistent and the corresponding confidence-interval estimator is asymptotically exact. If the NHPP has a piecewise constant intensity (rate) function, then the proposed point and confidence-interval estimators for the cumulative intensity function are valid over the entire time horizon and not just at the subinterval endpoints; and in this case algorithms are presented for generating event times from the estimated NHPP. Event count data from a call center illustrate the point and interval estimators.
Citation Details
Title: Nonparametric estimation and variate generation for a nonhomogeneous Poisson process from event count data.(TECHNICAL NOTE)
Author: Lawrence M. Leemis
Publication:IIE Transactions (Refereed)
Date: December 1, 2004
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: 36 Issue: 12 Page: 1155(6)
Distributed by Thomson Gale
From the author: Given a finite time horizon that has been partitioned into subintervals over which event counts have been accumulated for multiple realizations of a population NonHomogeneous Poisson Process (NHPP), this paper develops point and confidence-interval estimators for the cumulative intensity (or mean value) function of the population process evaluated at each subinterval endpoint. As the number of realizations tends to infinity, each point estimator is strongly consistent and the corresponding confidence-interval estimator is asymptotically exact. If the NHPP has a piecewise constant intensity (rate) function, then the proposed point and confidence-interval estimators for the cumulative intensity function are valid over the entire time horizon and not just at the subinterval endpoints; and in this case algorithms are presented for generating event times from the estimated NHPP. Event count data from a call center illustrate the point and interval estimators.
Citation Details
Title: Nonparametric estimation and variate generation for a nonhomogeneous Poisson process from event count data.(TECHNICAL NOTE)
Author: Lawrence M. Leemis
Publication:IIE Transactions (Refereed)
Date: December 1, 2004
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: 36 Issue: 12 Page: 1155(6)
Distributed by Thomson Gale
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