**Mon. 3:30-4:30pm, Rm MTH 0104
Fall 2017**

**Eric
Slud Paul Smith
Statistics Program ,
Math Department**

Interested participants should send email to
**evs@math.umd.edu** or **pjs@math.umd.edu**

**Research Focus:** This semester's Statistics RIT will be on the topic of Statistical Inference for Spatial Statistical Models. This concerns analysis of
datasets distributed in space (the surface of the earth, in **R ^{2}**, as in

plus many other topics. We will study chapters from a Handbook or textbook and journal papers from a few of these areas, focusing on areas of interest to the RIT
attendees.

**Graduate Prerequisites:** To benefit from this research activity, a graduate student should have completed Stat 700-701 and Stat 600.

**Graduate Program:** Graduate students will be involved in
reading and presenting book-chapters and papers from the statistical literature
concerning provable all aspects of Spatial Statistics.

**Work Schedule:** We will meet weekly in the fall of 2017, Mondays at 3:30-4:30 pm. The basic textbook background material for this RIT will be taken from
will be drawn from

Ribeiro, P. and Diggle, P., **Model Based Geostatistics** (2007) (on line at
** https://link.springer.com/book/10.1007%2F978-0-387-48536-2 **)
and

Gaetan, C. and Guyon, X. (2010), **Spatial Statistics and Modeling** (on line at
** https://link.springer.com/book/10.1007%2F978-0-387-92257-7 **)

Material for talks can also be drawn from

**Handbook of Spatial Statistics** (2010), eds. A. Gelfand, P. Diggle, M. Fuentes, and
P. Guttorp, CRC / Chapman & Hall.

and as an older supplementary reference,

N. Cressie (2015), **Statistics for Spatial Data**, revised edition, Wiley.

The Handbook cited above has useful chapters on "Classical Geiostatistical Methods" (by D. Zimmermann and M. Stein), on "Likelihood-based Methods" (by D. Zimmermann), on "Spectral Domain" (by M. Fuentes), on "Asymptotics for Spatial Processes" (by M. Stein), on "Hierarchical Modeling with Spatial Data" (by C. Wikle), on "Non-Gaussian and Nonparametric Models for Continuous Spatial Data" (by M. Steel and M. Fuentes), on "Discrete Spatial Variation" (by H. Rue and L. Held), on "Conditional and Intrinsic Autoregressions" (by L. Held and H. Rue), on "Disease Mapping" (by L. Waller and B. Carlin) and on "Spatial Econometrics" (by R. Pace and J. LeSage).

Besag, J. and Kooperberg, C. (1995), On conditional and inrinsic autoregressions, Biometrika 82, 733-746.

Bradley, R.C. and Tone, C. (2015), A central limit theorem for non-stationary strong mixing random fields. Journal of Theoretical Probability.

Cressie, N. and Lahiri, S. (1996), Asymptotics for REML estimation of spatial covariance parameters, Jour.~Statist.~Planning \& Inference 50, 327-341.

Kaiser, M.S., Lahiri, S.N. and Nordman, D. (2012). A goodness of fit test for conditionally specified spatial models and its asymptotic properties. Annals of Statistics 40 104-130.

Katzfuss, M., Stroud, J. and Wikle, C. (2016) Understanding the ensemble Kalman filter, American Statistician 70, 350-357.

Kent, J. and Mardia, K. (1996), Spectral and circulant approximations to the likelihood for stationary Gaussian random fields, Jour.~Statist.~Planning & Inference 50, 379-394.

Lahiri, S.N. (1996). Asymptotic expansions for sums of random vectors under polynomial mixing rates. Sankhya, Series A 58, 206 - 224.

Lahiri, S.N. (1996). On inconsistency of estimators under infill asymptotics for spatial data. Sankhya, Series A 58, 403-417.

Lahiri, S.N. (2003). Central Limit Theorems for weighted sums under some stochastic and fixed spatial sampling designs. Sankhya, Ser. A 65, 356-388.

Mardia, K. and Marshall, R. (1984), Maximum likelihood estimation of models for residual covariance in spatial statistics, Biometrika 71, 135-146.

Zhang, H. and Zimmermann, D. (2005), Towards reconciling two asymptotic frameworks in spatial statistics, Biometrika 92, 921-936.

reference to 3 papers: a tutorial on Bayesian optimization, one on computer experiments, and one on efficient global optimization.

additional paper on Non-Gaussian Data Assimilation.

© Last updated Nov. 29, 2017.