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

PSC In The News

RSS Feed icon

Former trainee Herbert says residential squatters may be a good thing

Work by Couper, Farley et al. shows impact of racial composition on neighborhood choice

Thompson details killings and shaping of official narrative in 1971 Attica prison uprising

More News

Highlights

Michigan ranked #12 on Business Insider's list of 50 best American colleges

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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