Analysis of Interval-Censored or Aggregated Point Process Data: A Local Likelihood Approach - Softcover
Synopsis
The use of local likelihood methods (Tibshirani and Hastie, 1987; Loader, 1996) in the presence of interval-censored or aggregated data leads to a natural consideration of an EM-type strategy, or a local EM algorithm. We consider local EM to analyze point process data that are either interval-censored or aggregated into counts. We present the formulation of local EM algorithms to estimate density, intensity and risk and their implementations using piecewise constant functions. It is shown that the use of the piecewise constant function at the E-step explicitly results in an iteration that involves an expectation, maximization and smoothing step, or an EMS algorithm considered in Silverman et al. (1990). Consequently, we reveal a previously unknown connection between local EM and the EMS algorithm. From a theoretical perspective, local EM and the EMS algorithm complement each other. We demonstrate that an EMS algorithm rises naturally from a local likelihood consideration in the context of point processes while the EMS algorithm serves as a rapid implementation of local EM algorithms and provides theoretical tools to better understand the role of local EM.
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Analysis of Interval-Censored or Aggregated Point Process Data
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VDM Verlag Dr. Müller, 2010
ISBN 10: 3639252543
ISBN 13: 9783639252545
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Fan Chun-Po SteveChun-Po Steve Fan graduated from Queen s University, Canada in 2003 with bachelor degrees in Economics and Statistics. He continued his graduate study in Statistics at the University of Toronto and graduated with. Seller Inventory # 4971154
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Analysis of Interval-Censored or Aggregated Point Process Data : A Local Likelihood Approach
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The use of local likelihood methods (Tibshirani and Hastie, 1987; Loader, 1996) in the presence of interval-censored or aggregated data leads to a natural consideration of an EM-type strategy, or a local EM algorithm. We consider local EM to analyze point process data that are either interval-censored or aggregated into counts. We present the formulation of local EM algorithms to estimate density, intensity and risk and their implementations using piecewise constant functions. It is shown that the use of the piecewise constant function at the E-step explicitly results in an iteration that involves an expectation, maximization and smoothing step, or an EMS algorithm considered in Silverman et al. (1990). Consequently, we reveal a previously unknown connection between local EM and the EMS algorithm. From a theoretical perspective, local EM and the EMS algorithm complement each other. We demonstrate that an EMS algorithm rises naturally from a local likelihood consideration in the context of point processes while the EMS algorithm serves as a rapid implementation of local EM algorithms and provides theoretical tools to better understand the role of local EM. Seller Inventory # 9783639252545
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Analysis of Interval-Censored or Aggregated Point Process Data | A Local Likelihood Approach
Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Analysis of Interval-Censored or Aggregated Point Process Data | A Local Likelihood Approach | Chun-Po Steve Fan | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639252545 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 101167792