ILR School

University of Missouri Node

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Now showing 1 - 4 of 4
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    Summary of “Workshop on Spatial and Spatio-Temporal Design and Analysis for Official Statistics”
    Holan, Scott H.; Cressie, Noel; Wikle, Chris K.; Bradley, Jonathan R.; Simpson, Matthew (2016-09-30)
    On Friday, May 20 and Saturday, May 21, 2016, the Spatio-Temporal StatisticsNSF Census Research Network (STSN) at the University of Missouri hosted a workshop on spatial and spatio-temporal design and analysis for official statistics, sponsored by the NSF-Census Research Network (NCRN). This report is a summary of the discussions of each of the topics from the workshop
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    Presentation: Introduction to Stan for Markov Chain Monte Carlo
    Simpson, Matthew (2017-04-25)
    An introduction to Stan ( a probabilistic programming language that implements Hamiltonian Monte Carlo (HMC), variational Bayes, and (penalized) maximum likelihood estimation. Presentation given at the U.S. Census Bureau on April 25, 2017.
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    The NSF-Census Research Network in 2016: Taking stock, looking forward
    Vilhuber, Lars (2016-05-21)
    An overview of the activities of the NSF-Census Research Network as of 2016, given on Saturday, May 21, 2016, at a workshop on spatial and spatio-temporal design and analysis for official statistics, hosted by the Spatio-Temporal Statistics NSF Census Research Network (STSN) at the University of Missouri, and sponsored by the NSF-Census Research Network (NCRN)
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    Asymptotic Theory of Cepstral Random Fields
    McElroy, T.S.; Holan, S.H. (Annals of Statistics, 2013)
    Random fields play a central role in the analysis of spatially correlated data and, as a result,have a significant impact on a broad array of scientific applications. Given the importance of this topic, there has been a substantial amount of research devoted to this area. However, the cepstral random field model remains largely underdeveloped outside the engineering literature. We provide a comprehensive treatment of the asymptotic theory for two-dimensional random field models. In particular, we provide recursive formulas that connect the spatial cepstral coefficients to an equivalent moving-average random field, which facilitates easy computation of the necessary autocovariance matrix. Additionally, we establish asymptotic consistency results for Bayesian, maximum likelihood, and quasi-maximum likelihood estimation of random field parameters and regression parameters. Further, in both the maximum and quasi-maximum likelihood frameworks, we derive the asymptotic distribution of our estimator. The theoretical results are presented generally and are of independent interest,pertaining to a wide class of random field models. The results for the cepstral model facilitate model-building: because the cepstral coefficients are unconstrained in practice, numerical optimization is greatly simplified, and we are always guaranteed a positive definite covariance matrix. We show that inference for individual coefficients is possible, and one can refine models in a disciplined manner. Finally, our results are illustrated through simulation and the analysis of straw yield data in an agricultural field experiment.