Joint composite estimating functions in spatiotemporal models

Archived Abstract of Former PSC Researcher

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.

10.1111/j.1467-9868.2012.01035.x

Browse | Search | Next

PSC In The News

RSS Feed icon

Shaefer comments on the Cares Act impact in negating hardship during COVID-19 pandemic

Heller comments on lasting safety benefit of youth employment programs

More News

Highlights

Dean Yang's Combatting COVID-19 in Mozambique study releases Round 1 summary report

Help Establish Standard Data Collection Protocols for COVID-19 Research

More Highlights


Connect with PSC follow PSC on Twitter Like PSC on Facebook