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Activity Number: 251
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #304769
Title: Semiparametric Proportional Hazards Model with Spatially Correlated Random Effects, with an Application to the Survival of Loblolly Pine
Author(s): Jie Li*+ and Yili Hong and Ram Thapa and Harold E. Burkhart
Companies: Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech
Address: Department of Statistics, Blacksburg, VA, 24061, United States
Keywords: Cox model ; EM algorithm ; forestry ; spatial frailty ; time to event data ; wood industry

The loblolly pine, one of the native pine species of the southeastern US, is the most-planted species for commercial timber. The survival of loblolly pine is of interest to researchers in forestry science, because it is related to the yield of timber. Data were collected from the permanent plots established in 1980/81 at 182 locations ranging from central Texas east to Florida, and north to Delaware. Repeated measurements were made on trees every 3 years. One of the main objectives of this study was to explain the survival of loblolly pine using covariates such as thinning types, physiographic regions, while adjusting for possible spatial correlations among different locations. We introduce a semiparametric PH model with time dependent covariates to describe the effects of covariates on the survival time, with nonparametric baseline hazard function. We use spatial random effects to describe the spatial correlation. The spatial correction is modeled as a function of geographical distance. We develop an EM algorithm to estimate the parameters. We apply the developed method to the large-scale survival data and summarize our findings. We conclude this paper with some discussions.

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