Open Access
December 2008 State-space based mass event-history model I: Many decision-making agents with one target
Hsieh Fushing, Li Zhu, David I. Shapiro-Ilan, James F. Campbell, Edwin E. Lewis
Ann. Appl. Stat. 2(4): 1503-1522 (December 2008). DOI: 10.1214/08-AOAS189


A dynamic decision-making system that includes a mass of indistinguishable agents could manifest impressive heterogeneity. This kind of nonhomogeneity is postulated to result from macroscopic behavioral tactics employed by almost all involved agents. A State-Space Based (SSB) mass event-history model is developed here to explore the potential existence of such macroscopic behaviors. By imposing an unobserved internal state-space variable into the system, each individual’s event-history is made into a composition of a common state duration and an individual specific time to action. With the common state modeling of the macroscopic behavior, parametric statistical inferences are derived under the current-status data structure and conditional independence assumptions. Identifiability and computation related problems are also addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB mass event-history model is shown to be very distinct from a random effect model via the Principle Component Analysis (PCA) in a numerical experiment. Real data showing the mass invasion by two species of parasitic nematode into two species of host larvae are also analyzed. The analysis results not only are found coherent in the context of the biology of the nematode as a parasite, but also include new quantitative interpretations.


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Hsieh Fushing. Li Zhu. David I. Shapiro-Ilan. James F. Campbell. Edwin E. Lewis. "State-space based mass event-history model I: Many decision-making agents with one target." Ann. Appl. Stat. 2 (4) 1503 - 1522, December 2008.


Published: December 2008
First available in Project Euclid: 8 January 2009

zbMATH: 1156.62006
MathSciNet: MR2655669
Digital Object Identifier: 10.1214/08-AOAS189

Keywords: Extremists , Heterogeneity , interval censoring , logistic regression , maximum likelihood estimation , Nematode , Parasite infection , Weibull distribution

Rights: Copyright © 2008 Institute of Mathematical Statistics

Vol.2 • No. 4 • December 2008
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