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

PSC In The News

RSS Feed icon

Buchmueller says employee wages are hit harder than corporate profits by rising health insurance costs

Davis-Kean et al. link children's self-perceptions to their math and reading achievement

Yang and Mahajan examine how hurricanes impact migration to the US

More News

Highlights

Pamela Smock elected to PAA Committee on Publications

Viewing the eclipse from ISR-Thompson

Paula Fomby to succeed Jennifer Barber as Associate Director of PSC

PSC community celebrates Violet Elder's retirement from PSC

More Highlights

Next Brown Bag

Mon, Sept 11, 2017, noon:
Welcoming of Postdoctoral Fellows: Angela Bruns, Karra Greenberg, Sarah Seelye and Emily Treleaven

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