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We consider the effective sample size, based on Godambe information, forblock likelihood inference which is an attractive and computationally feasiblealternative to full likelihood inference for large correlated datasets. Withreference to a Gaussian random field having a constant mean, we explore how thechoice of blocks impacts this effective sample size. It is seen that spreadingout the spatial points within each block, instead of keeping them closetogether, can lead to considerable gains while retaining computationalsimplicity. Analytical results in this direction are obtained under the AR(1)model. The insights so found facilitate the study of other models, includingcorrelation models on a plane, where closed form expressions are intractable.

Improving upon the effective sample size based on Godambe information for block likelihood inference