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

PSC In The News

RSS Feed icon

Stern, Novak, Harlow, and colleagues say compensation due Californians forcibly sterilized under eugenics laws

Burgard and Seelye find job insecurity linked to psychological distress among workers in later years

Former PSC trainee Jay Borchert parlays past incarceration and doctoral degree into pursuing better treatment of inmates

More News


Savolainen wins Outstanding Contribution Award for study of how employment affects recidivism among past criminal offenders

Giving Blueday at ISR focuses on investing in the next generation of social scientists

Pfeffer and Schoeni cover the economic and social dimensions of wealth inequality in this special issue

PRB Policy Communication Training Program for PhD students in demography, reproductive health, population health

More Highlights

Next Brown Bag

Mon, Jan 23, 2017 at noon:
H. Luke Shaefer

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