Analysis Of Intervalcensored Or Aggregated Point Process Data: A Local Likelihood Approach,Used

Analysis Of Intervalcensored Or Aggregated Point Process Data: A Local Likelihood Approach,Used

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The use of local likelihood methods (Tibshirani and Hastie, 1987; Loader, 1996) in the presence of intervalcensored or aggregated data leads to a natural consideration of an EMtype strategy, or a local EM algorithm. We consider local EM to analyze point process data that are either intervalcensored 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 Estep 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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To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.

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We provide a 2-year limited warranty, from the date of purchase for all our products.

If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.

This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).

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