JSM 2005 Online Program

Abstract #302590

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	436
Type:	Invited
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Biometrics Section
Abstract - #302590
Title:	Modeling of Hormone Secretion with Marked Nonhomogeneous Poisson Process
Author(s):	Anna Liu*+ and Yuedong Wang
Companies:	University of Massachusetts and University of California, Santa Barbara
Address:	Department of Mathematics and Statistics, Amherst, MA, 01003,
Keywords:	birth-death MCMC ; endocrine data ; time associations ; pulse associations
Abstract:	It is commonly believed that hormones are secreted in basal and pulsatile manners. The level of the basal secretion, the pulse frequency, the hormone decay rate from peak to nadir, and the pulse amplitude are characteristics of hormone series. How they are altered by various diseases is of interest to medical researchers. Most current approaches require identification of pulse locations in order to estimate these parameters, which is not easy as the pulse signals often are masked due to the slow hormone decay rate. Assuming the pulses are generated from a nonhomogeneous Poisson process, this paper uses a stochastic model that convolves a pulse shape function and the Poisson process, thus bypassing the requirement of knowing pulse locations. Simultaneous inference on the parameters is based on a normal approximation to the likelihood of the stochastic model. The unknown intensity function of the Poisson process is estimated nonparametrically based on the penalized quasi-likelihood approach. Both simulations and applications suggest the method provides reasonable estimates and valid inferences.

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