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Activity Number: 17
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #315492
Title: Likelihood Approximation and Model Quality Assessment for Large Environmental Data Sets
Author(s): Ying Sun* and Michael L. Stein
Companies: King Abdullah University of Science and Technology and The University of Chicago
Keywords: Composite likelihood ; Iterative method ; Sparse matrices ; Precision matrices ; Statistical efficiency ; Unbiased estimating equations
Abstract:

For Gaussian process models, likelihood based methods are often difficult to use with large irregularly spaced spatial datasets. This talk focuses on statistical methods for fitting Gaussian process models to large environmental datasets. The proposed new method is based on score equation approximation that leads to computationally and statistically efficient estimating equations. The covariance matrix inverse, or the precision matrix, that appears in the score equation, is approximated by adapting the composite likelihood method. Different types of approximations are considered and measures for model quality assessment are also discussed to compare the statistical efficiency.


Authors who are presenting talks have a * after their name.

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