Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Kusunoki, Hall, and Barber find obese teen girls less likely to use birth control

Prescott finds reported sex offenses lower in neighborhoods with resident sex offenders

Geronimus says poor Detroiters face greater health risks given adverse social conditions

Highlights

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

David Lam is new director of Institute for Social Research

Elizabeth Bruch wins Robert Merton Prize for paper in analytic sociology

Elizabeth Bruch wins ASA award for paper in mathematical sociology

Next Brown Bag

PSC Brown Bags will be back fall 2015


Joint composite estimating functions in spatiotemporal models

Publication Abstract

Bai, Y., P.K. Song, and Trivellore Raghunathan. 2012. "Joint composite estimating functions in spatiotemporal models." Journal of the Royal Statistical Society Series B-Statistical Methodology, 74: 799-824.

Modelling of spatiotemporal processes has received considerable attention in recent statistical research. However, owing to the high dimensionality of the data, the joint modelling of spatial and temporal processes presents a great computational challenge, in both likelihood-based and Bayesian approaches. We propose a joint composite estimating function approach to estimating spatiotemporal covariance structures. This substantially reduces the computational complexity and is more efficient than existing composite likelihood methods. The novelty of the proposed joint composite estimating function is rooted in the construction of three sets of estimating functions from spatial, temporal and spatiotemporal cross-pairs, which results in overidentified estimating functions. Thus, we form a joint inference function in a spirit that is similar to Hansen's generalized method of moments. We show that under practical scenarios the estimator proposed is consistent and asymptotically normal. Simulation studies prove that our method performs well in finite samples. Finally, we illustrate the joint composite estimating function method by estimating the spatiotemporal dependence structure of airborne particulates (PM10) in the north-eastern USA over a 32-month period.

DOI:10.1111/j.1467-9868.2012.01035.x (Full Text)

Browse | Search : All Pubs | Next