@article {2221, title = {Bayesian Lattice Filters for Time-Varying Autoregression and Time{\textendash}Frequency Analysis}, journal = {Project Euclid}, year = {2015}, month = {10/2015}, pages = {27}, abstract = {Modeling nonstationary processes is of paramount importance to many scientific disciplines including environmental science, ecology, and finance, among others. Consequently, flexible methodology that provides accurate estimation across a wide range of processes is a subject of ongoing interest. We propose a novel approach to model-based time{\textendash}frequency estimation using time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our estimation method uses the lattice filter and is cast within the partial autocorrelation domain. The marginal posterior distributions are of standard form and, as a convenient by-product of our estimation method, our approach avoids undesirable matrix inversions. As such, estimation is extremely computationally efficient and stable. To illustrate the effectiveness of our approach, we conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through three modeling applications; namely, insect communication signals, environmental data (wind components), and macroeconomic data (US gross domestic product (GDP) and consumption).}, keywords = {locally stationary, model selection, nonstationary partial autocorrelation, piecewise stationary, sequential estimation, time-varying spectral density}, doi = {10.1214/15-BA978}, url = {http://projecteuclid.org/euclid.ba/1445263834}, author = {Yang, W.~H. and Holan, Scott H. and Wikle, Christopher K.} } @article {2039, title = {Bayesian Marked Point Process Modeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography}, journal = {Spatial Statistics}, volume = {14}, year = {2015}, month = {08/2015}, pages = {439--451}, doi = {10.1016/j.spasta.2015.07.008}, url = {http://www.sciencedirect.com/science/article/pii/S2211675315000718}, author = {Quick, Harrison and Holan, Scott H. and Wikle, Christopher K. and Reiter, Jerome P.} } @article {2219, title = {Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey}, journal = {Journal of the American Statistical Association}, year = {2015}, month = {12/2015}, abstract = {We introduce Bayesian spatial change of support methodology for count-valued survey data with known survey variances. Our proposed methodology is motivated by the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that provides timely information on several key demographic variables. Specifically, the ACS produces 1-year, 3-year, and 5-year {\textquotedblleft}period-estimates,{\textquotedblright} and corresponding margins of errors, for published demographic and socio-economic variables recorded over predefined geographies within the United States. Despite the availability of these predefined geographies it is often of interest to data-users to specify customized user-defined spatial supports. In particular, it is useful to estimate demographic variables defined on {\textquotedblleft}new{\textquotedblright} spatial supports in {\textquotedblleft}real-time.{\textquotedblright} This problem is known as spatial change of support (COS), which is typically performed under the assumption that the data follows a Gaussian distribution. However, count-valued survey data is naturally non-Gaussian and, hence, we consider modeling these data using a Poisson distribution. Additionally, survey-data are often accompanied by estimates of error, which we incorporate into our analysis. We interpret Poisson count-valued data in small areas as an aggregation of events from a spatial point process. This approach provides us with the flexibility necessary to allow ACS users to consider a variety of spatial supports in {\textquotedblleft}real-time.{\textquotedblright} We show the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data.}, keywords = {Aggregation, American Community Survey, Bayesian hierarchical model, Givens angle prior, Markov chain Monte Carlo, Multiscale model, Non-Gaussian.}, doi = {10.1080/01621459.2015.1117471}, url = {http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1117471}, author = {Bradley, Jonathan R. and Wikle, Christopher K. and Holan, Scott H.} } @techreport {handle:1813:40176, title = {NCRN Meeting Spring 2015: Models for Multiscale Spatially-Referenced Count Data}, number = {1813:40176}, year = {2015}, institution = {NCRN Coordinating Office}, type = {Preprint}, abstract = {NCRN Meeting Spring 2015: Models for Multiscale Spatially-Referenced Count Data Holan, Scott; Bradley, Jonathan R.; Wikle, Christopher K. Presentation at the NCRN Meeting Spring 2015}, url = {http://hdl.handle.net/1813/40176}, author = {Holan, Scott and Bradley, Jonathan R. and Wikle, Christopher K.} } @techreport {handle:1813:40177, title = {NCRN Meeting Spring 2015: Regionalization of Multiscale Spatial Processes Using a Criterion for Spatial Aggregation Error}, number = {1813:40177}, year = {2015}, institution = {NCRN Coordinating Office}, type = {Preprint}, abstract = {NCRN Meeting Spring 2015: Regionalization of Multiscale Spatial Processes Using a Criterion for Spatial Aggregation Error Wikle, Christopher K.; Bradley, Jonathan; Holan, Scott Develop and implement a statistical criterion to diagnose spatial aggregation error that can facilitate the choice of regionalizations of spatial data. Presentation at NCRN Meeting Spring 2015}, url = {http://hdl.handle.net/1813/40177}, author = {Wikle, Christopher K. and Bradley, Jonathan and Holan, Scott} } @techreport {handle:1813:40179, title = {NCRN Meeting Spring 2015: Training Undergraduates, Graduate Students, Postdocs, and Federal Agencies: Methodology, Data, and Science for Federal Statistics}, number = {1813:40179}, year = {2015}, institution = {NCRN Coordinating Office}, type = {Preprint}, abstract = {NCRN Meeting Spring 2015: Training Undergraduates, Graduate Students, Postdocs, and Federal Agencies: Methodology, Data, and Science for Federal Statistics Cressie, Noel; Holan, Scott H.; Wikle, Christopher K. Presentation at the NCRN Spring 2015 Meeting}, url = {http://hdl.handle.net/1813/40179}, author = {Cressie, Noel and Holan, Scott H. and Wikle, Christopher K.} } @article {STA4:STA494, title = {Spatio-temporal change of support with application to American Community Survey multi-year period estimates}, journal = {Stat}, volume = {4}, year = {2015}, month = {10/2015}, pages = {255{\textendash}270}, abstract = {We present hierarchical Bayesian methodology to perform spatio-temporal change of support (COS) for survey data with Gaussian sampling errors. This methodology is motivated by the American Community Survey (ACS), which is an ongoing survey administered by the US Census Bureau that provides timely information on several key demographic variables. The ACS has published 1-year, 3-year, and 5-year period estimates, and margins of errors, for demographic and socio-economic variables recorded over predefined geographies. The spatio-temporal COS methodology considered here provides data users with a way to estimate ACS variables on customized geographies and time periods while accounting for sampling errors. Additionally, 3-year ACS period estimates are to be discontinued, and this methodology can provide predictions of ACS variables for 3-year periods given the available period estimates. The methodology is based on a spatio-temporal mixed-effects model with a low-dimensional spatio-temporal basis function representation, which provides multi-resolution estimates through basis function aggregation in space and time. This methodology includes a novel parameterization that uses a target dynamical process and recently proposed parsimonious Moran{\textquoteright}s I propagator structures. Our approach is demonstrated through two applications using public-use ACS estimates and is shown to produce good predictions on a hold-out set of 3-year period estimates. Copyright {\textcopyright} 2015 John Wiley \& Sons, Ltd.}, keywords = {Bayesian, change-of-support, dynamical, hierarchical models, mixed-effects model, Moran{\textquoteright}s I, multi-year period estimate}, issn = {2049-1573}, doi = {10.1002/sta4.94}, url = {http://dx.doi.org/10.1002/sta4.94}, author = {Bradley, Jonathan R. and Wikle, Christopher K. and Holan, Scott H.} }