A Particle Filter Approach to Multiprocess Dynamic Models with Application to Hormone Data

Research output: Contribution to journalArticle

Abstract

We extend the multiprocess dynamic models to the general non-Gaussian and nonlinear setting. Under this framework, we propose specific models to simultaneously model hormone smooth basal trend and pulsatile activities. The pulse input is modeled by two processes: one as a point mass at zero and one as a gamma distributed random variable. This gamma-driven approach ensures the pulse estimates to be nonnegative, which is an intrinsic characteristic of hormone dynamics. The smooth trend is modeled by smoothing splines. Both additive and multiplicative observational errors are investigated. Parameters are estimated by maximizing the marginal likelihood. Baseline and pulses are estimated by posterior means. For implementation, particle filter is adopted. Unlike the traditional condensation method where a single distribution is used to approximate a mixture of distributions, this particle filter approach allows the model components to be accurately evaluated at the expense of computational resources. The specific models are applied to a cortisol series. The finite sample performance is evaluated by a simulation. The data application and the simulation show that the biological characteristics can be incorporated and be accurately estimated under the proposed framework.

Original languageEnglish (US)
Pages (from-to)379-393
Number of pages15
JournalStatistics in Biosciences
Volume7
Issue number2
DOIs
StatePublished - Feb 25 2015

Fingerprint

Particle Filter
Hormones
Dynamic models
Dynamic Model
Posterior Mean
Mixture of Distributions
Hydrocortisone
Marginal Likelihood
Smoothing Splines
Component Model
Condensation
Baseline
Multiplicative
Simulation
Random variable
Non-negative
Random variables
Model
Splines
Resources

Keywords

  • Hormone pulses
  • Multiprocess dynamic models
  • Particle filter
  • Smooth baseline
  • State space

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Statistics and Probability

Cite this

A Particle Filter Approach to Multiprocess Dynamic Models with Application to Hormone Data. / Liu, Ziyue.

In: Statistics in Biosciences, Vol. 7, No. 2, 25.02.2015, p. 379-393.

Research output: Contribution to journalArticle

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