TY - JOUR T1 - Adaptively-Tuned Particle Swarm Optimization with Application to Spatial Design JF - Stat Y1 - 2017 A1 - Simpson, M. A1 - Wikle, C.K. A1 - Holan, S.H. AB - Particle swarm optimization (PSO) algorithms are a class of heuristic optimization algorithms that are attractive for complex optimization problems. We propose using PSO to solve spatial design problems, e.g. choosing new locations to add to an existing monitoring network. Additionally, we introduce two new classes of PSO algorithms that perform well in a wide variety of circumstances, called adaptively tuned PSO and adaptively tuned bare bones PSO. To illustrate these algorithms, we apply them to a common spatial design problem: choosing new locations to add to an existing monitoring network. Specifically, we consider a network in the Houston, TX, area for monitoring ambient ozone levels, which have been linked to out-of-hospital cardiac arrest rates. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA VL - 6 UR - http://onlinelibrary.wiley.com/doi/10.1002/sta4.142/abstract IS - 1 ER - TY - RPRT T1 - Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data. (With Discussion). Y1 - 2017 A1 - Bradley, J.R. A1 - Holan, S.H. A1 - Wikle, C.K. KW - Aggregation KW - American Community Survey KW - Bayesian hierarchical model KW - Big Data KW - Longitudinal Employer-Household Dynamics (LEHD) program KW - Markov chain Monte Carlo KW - Non-Gaussian. KW - Quarterly Workforce Indicators AB - We introduce a Bayesian approach for multivariate spatio-temporal prediction for high-dimensional count-valued data. Our primary interest is when there are possibly millions of data points referenced over different variables, geographic regions, and times. This problem requires extensive methodological advancements, as jointly modeling correlated data of this size leads to the so-called "big n problem." The computational complexity of prediction in this setting is further exacerbated by acknowledging that count-valued data are naturally non-Gaussian. Thus, we develop a new computationally efficient distribution theory for this setting. In particular, we introduce a multivariate log-gamma distribution and provide substantial theoretical development including: results regarding conditional distributions, marginal distributions, an asymptotic relationship with the multivariate normal distribution, and full-conditional distributions for a Gibbs sampler. To incorporate dependence between variables, regions, and time points, a multivariate spatio-temporal mixed effects model (MSTM) is used. The results in this manuscript are extremely general, and can be used for data that exhibit fewer sources of dependency than what we consider (e.g., multivariate, spatial-only, or spatio-temporal-only data). Hence, the implications of our modeling framework may have a large impact on the general problem of jointly modeling correlated count-valued data. We show the effectiveness of our approach through a simulation study. Additionally, we demonstrate our proposed methodology with an important application analyzing data obtained from the Longitudinal Employer-Household Dynamics (LEHD) program, which is administered by the U.S. Census Bureau. JF - arXiv UR - https://arxiv.org/abs/1512.07273 ER - TY - JOUR T1 - Regionalization of Multiscale Spatial Processes using a Criterion for Spatial Aggregation Error JF - Journal of the Royal Statistical Society -- Series B. Y1 - 2017 A1 - Bradley, J.R. A1 - Wikle, C.K. A1 - Holan, S.H. KW - American Community Survey KW - empirical orthogonal functions KW - MAUP KW - Reduced rank KW - Spatial basis functions KW - Survey data AB - The modifiable areal unit problem and the ecological fallacy are known problems that occur when modeling multiscale spatial processes. We investigate how these forms of spatial aggregation error can guide a regionalization over a spatial domain of interest. By "regionalization" we mean a specification of geographies that define the spatial support for areal data. This topic has been studied vigorously by geographers, but has been given less attention by spatial statisticians. Thus, we propose a criterion for spatial aggregation error (CAGE), which we minimize to obtain an optimal regionalization. To define CAGE we draw a connection between spatial aggregation error and a new multiscale representation of the Karhunen-Loeve (K-L) expansion. This relationship between CAGE and the multiscale K-L expansion leads to illuminating theoretical developments including: connections between spatial aggregation error, squared prediction error, spatial variance, and a novel extension of Obled-Creutin eigenfunctions. The effectiveness of our approach is demonstrated through an analysis of two datasets, one using the American Community Survey and one related to environmental ocean winds. UR - https://arxiv.org/abs/1502.01974 ER - TY - JOUR T1 - Visualizing uncertainty in areal data estimates with bivariate choropleth maps, map pixelation, and glyph rotation JF - Stat Y1 - 2017 A1 - Lucchesi, L.R. A1 - Wikle, C.K. AB - In statistics, we quantify uncertainty to help determine the accuracy of estimates, yet this crucial piece of information is rarely included on maps visualizing areal data estimates. We develop and present three approaches to include uncertainty on maps: (1) the bivariate choropleth map repurposed to visualize uncertainty; (2) the pixelation of counties to include values within an estimate's margin of error; and (3) the rotation of a glyph, located at a county's centroid, to represent an estimate's uncertainty. The second method is presented as both a static map and visuanimation. We use American Community Survey estimates and their corresponding margins of error to demonstrate the methods and highlight the importance of visualizing uncertainty in areal data. An extensive online supplement provides the R code necessary to produce the maps presented in this article as well as alternative versions of them. VL - 6 UR - http://onlinelibrary.wiley.com/doi/10.1002/sta4.150/abstract IS - 1 ER - TY - JOUR T1 - Bayesian Hierarchical Models with Conjugate Full-Conditional Distributions for Dependent Data from the Natural Exponential Family JF - Journal of the American Statistical Association - T&M. Y1 - 2016 A1 - Bradley, J.R. A1 - Holan, S.H. A1 - Wikle, C.K. AB - We introduce a Bayesian approach for analyzing (possibly) high-dimensional dependent data that are distributed according to a member from the natural exponential family of distributions. This problem requires extensive methodological advancements, as jointly modeling high-dimensional dependent data leads to the so-called "big n problem." The computational complexity of the "big n problem" is further exacerbated when allowing for non-Gaussian data models, as is the case here. Thus, we develop new computationally efficient distribution theory for this setting. In particular, we introduce something we call the "conjugate multivariate distribution," which is motivated by the univariate distribution introduced in Diaconis and Ylvisaker (1979). Furthermore, we provide substantial theoretical and methodological development including: results regarding conditional distributions, an asymptotic relationship with the multivariate normal distribution, conjugate prior distributions, and full-conditional distributions for a Gibbs sampler. The results in this manuscript are extremely general, and can be adapted to many different settings. We demonstrate the proposed methodology through simulated examples and analyses based on estimates obtained from the US Census Bureaus' American Community Survey (ACS). UR - https://arxiv.org/abs/1701.07506 ER - TY - JOUR T1 - Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis JF - Bayesian Analysis Y1 - 2016 A1 - Yang, W.H. A1 - Holan, S.H. A1 - Wikle, C.K. AB - 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-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). UR - https://arxiv.org/abs/1408.2757 ER - TY - JOUR T1 - Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey JF - Journal of the American Statistical Association Y1 - 2016 A1 - Bradley, J.R. A1 - Wikle, C.K. A1 - Holan, S.H. AB - 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 "period-estimates," 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 "new" spatial supports in "real-time." 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 "real-time." We demonstrate the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data. UR - https://arxiv.org/abs/1405.7227 ER - TY - JOUR T1 - Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing JF - Journal of the Royal Statistical Society - Series A Y1 - 2016 A1 - Quick, H. A1 - Holan, S.H. A1 - Wikle, C.K. AB - When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies prior to making data publicly available due to data privacy obligations. An alternative to releasing aggregated and/or perturbed data is to release multiply-imputed synthetic data, where sensitive values are replaced with draws from statistical models designed to capture important distributional features in the collected data. One issue that has received relatively little attention, however, is how to handle spatially outlying observations in the collected data, as common spatial models often have a tendency to overfit these observations. The goal of this work is to bring this issue to the forefront and propose a solution, which we refer to as "differential smoothing." After implementing our method on simulated data, highlighting the effectiveness of our approach under various scenarios, we illustrate the framework using data consisting of sale prices of homes in San Francisco. UR - https://arxiv.org/abs/1507.05529 ER - TY - JOUR T1 - Bayesian Analysis of Spatially-Dependent Functional Responses with Spatially-Dependent Multi-Dimensional Functional Predictors JF - Statistica Sinica Y1 - 2015 A1 - Yang, W. H. A1 - Wikle, C.K. A1 - Holan, S.H. A1 - Sudduth, K. A1 - Meyers, D.B. VL - 25 UR - http://www3.stat.sinica.edu.tw/preprint/SS-13-245w_Preprint.pdf ER - TY - JOUR T1 - Bayesian Binomial Mixture Models for Estimating Abundance in Ecological Monitoring Studies JF - Annals of Applied Statistics Y1 - 2015 A1 - Wu, G. A1 - Holan, S.H. A1 - Nilon, C.H. A1 - Wikle, C.K. VL - 9 UR - http://projecteuclid.org/euclid.aoas/1430226082 ER - TY - JOUR T1 - Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis JF - ArXiv Y1 - 2015 A1 - Yang, W. H. A1 - Holan, S. H. A1 - Wikle, C.K. AB - 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-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). UR - http://arxiv.org/abs/1408.2757 IS - 1408.2757 ER - TY - JOUR T1 - Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models JF - Journal of Statistical Planning and Inference Y1 - 2015 A1 - Porter, A.T. A1 - Holan, S.H. A1 - Wikle, C.K. VL - 165 ER - TY - JOUR T1 - Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey JF - Journal of the American Statistical Association Y1 - 2015 A1 - Bradley, Jonathan A1 - Wikle, C.K. A1 - Holan, S. H. AB - 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 “period-estimates,” 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 “new” spatial supports in “real-time.” 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 “real-time.” We show the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data. UR - http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1117471 ER - TY - JOUR T1 - Bayesian Spatial Change of Support for Count–Valued Survey Data JF - ArXiv Y1 - 2015 A1 - Bradley, J. R. A1 - Wikle, C.K. A1 - Holan, S. H. AB - 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 "period-estimates," 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 "new" spatial supports in "real-time." 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 "real-time." We demonstrate the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data. UR - http://arxiv.org/abs/1405.7227 IS - 1405.7227 ER - TY - JOUR T1 - Comment on ``Semiparametric Bayesian Density Estimation with Disparate Data Sources: A Meta-Analysis of Global Childhood Undernutrition" by Finncane, M. M., Paciorek, C. J., Stevens, G. A., and Ezzati, M. JF - Journal of the American Statistical Association Y1 - 2015 A1 - Wikle, C.K. A1 - Holan, S.H. ER - TY - CHAP T1 - Hierarchcial models for uncertainty quantification: An overview T2 - Handbook of Uncertainty Quantification Y1 - 2015 A1 - Wikle, C.K. ED - Ghanem, R. ED - Higdon, D. ED - Owhadi, H. JF - Handbook of Uncertainty Quantification PB - Springer ER - TY - CHAP T1 - Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete Valued Data T2 - Handbook of Discrete-Valued Time Series Y1 - 2015 A1 - Wikle, C.K. A1 - Hooten, M.B. ED - Davis, R. ED - Holan, S. ED - Lund, R. ED - Ravishanker, N. JF - Handbook of Discrete-Valued Time Series PB - Chapman and Hall/CRC Press CY - Boca Raton, FL. UR - http://www.crcpress.com/product/isbn/9781466577732 ER - TY - CHAP T1 - Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data T2 - Handbook of Discrete-Valued Time Series Y1 - 2015 A1 - Holan, S.H. A1 - Wikle, C.K. ED - Davis, R. ED - Holan, S. ED - Lund, R. ED - Ravishanker, N JF - Handbook of Discrete-Valued Time Series PB - Chapman and Hall/CRC Press CY - Boca Raton, FL SN - ISBN 9781466577732 UR - http://www.crcpress.com/product/isbn/9781466577732 N1 - to appear in "Handbook of Discrete-Valued Time Series ER - TY - CHAP T1 - Hierarchical Dynamic Generalized Linear Mixed Models for Discrete--Valued Spatio-Temporal Data T2 - Handbook of Discrete--Valued Time Series Y1 - 2015 A1 - Holan, S.H. A1 - Wikle, C.K. JF - Handbook of Discrete--Valued Time Series ER - TY - CHAP T1 - Hierarchical Spatial Models T2 - Encyclopedia of Geographical Information Science Y1 - 2015 A1 - Arab, A. A1 - Hooten, M.B. A1 - Wikle, C.K. JF - Encyclopedia of Geographical Information Science PB - Springer ER - TY - JOUR T1 - Hierarchical, stochastic modeling across spatiotemporal scales of large river ecosystems and somatic growth in fish populations under various climate models: Missouri River sturgeon example JF - Geological Society Y1 - 2015 A1 - Wildhaber, M.L. A1 - Wikle, C.K. A1 - Moran, E.H. A1 - Anderson, C.J. A1 - Franz, K.J. A1 - Dey, R. ER - TY - JOUR T1 - Modern Perspectives on Statistics for Spatio-Temporal Data JF - WIRES Computational Statistics Y1 - 2015 A1 - Wikle, C.K. VL - 7 UR - http://dx.doi.org/10.1002/wics.1341 IS - 1 ER - TY - ICOMM T1 - Multiscale Analysis of Survey Data: Recent Developments and Exciting Prospects Y1 - 2015 A1 - Bradley, J.R. A1 - Wikle, C.K. A1 - Holan, S.H. JF - Statistics Views ER - TY - JOUR T1 - Multivariate Spatial Hierarchical Bayesian Empirical Likelihood Methods for Small Area Estimation JF - STAT Y1 - 2015 A1 - Porter, A.T. A1 - Holan, S.H. A1 - Wikle, C.K. VL - 4 UR - http://dx.doi.org/10.1002/sta4.81 IS - 1 ER - TY - JOUR T1 - Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynamics JF - ArXiv Y1 - 2015 A1 - Bradley, J. R. A1 - Holan, S. H. A1 - Wikle, C.K. AB - Many data sources report related variables of interest that are also referenced over geographic regions and time; however, there are relatively few general statistical methods that one can readily use that incorporate these multivariate spatio-temporal dependencies. Additionally, many multivariate spatio-temporal areal datasets are extremely high-dimensional, which leads to practical issues when formulating statistical models. For example, we analyze Quarterly Workforce Indicators (QWI) published by the US Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) program. QWIs are available by different variables, regions, and time points, resulting in millions of tabulations. Despite their already expansive coverage, by adopting a fully Bayesian framework, the scope of the QWIs can be extended to provide estimates of missing values along with associated measures of uncertainty. Motivated by the LEHD, and other applications in federal statistics, we introduce the multivariate spatio-temporal mixed effects model (MSTM), which can be used to efficiently model high-dimensional multivariate spatio-temporal areal datasets. The proposed MSTM extends the notion of Moran's I basis functions to the multivariate spatio-temporal setting. This extension leads to several methodological contributions including extremely effective dimension reduction, a dynamic linear model for multivariate spatio-temporal areal processes, and the reduction of a high-dimensional parameter space using {a novel} parameter model. UR - http://arxiv.org/abs/1503.00982 IS - 1503.00982 ER - TY - JOUR T1 - Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynamics JF - Annals of Applied Statistics Y1 - 2015 A1 - Bradley, J.R. A1 - Holan, S.H. A1 - Wikle, C.K. AB - Many data sources report related variables of interest that are also referenced over geographic regions and time; however, there are relatively few general statistical methods that one can readily use that incorporate these multivariate spatio-temporal dependencies. Additionally, many multivariate spatio-temporal areal datasets are extremely high-dimensional, which leads to practical issues when formulating statistical models. For example, we analyze Quarterly Workforce Indicators (QWI) published by the US Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program. QWIs are available by different variables, regions, and time points, resulting in millions of tabulations. Despite their already expansive coverage, by adopting a fully Bayesian framework, the scope of the QWIs can be extended to provide estimates of missing values along with associated measures of uncertainty. Motivated by the LEHD, and other applications in federal statistics, we introduce the multivariate spatio-temporal mixed effects model (MSTM), which can be used to efficiently model high-dimensional multivariate spatio-temporal areal datasets. The proposed MSTM extends the notion of Moran’s I basis functions to the multivariate spatio-temporal setting. This extension leads to several methodological contributions including extremely effective dimension reduction, a dynamic linear model for multivariate spatio-temporal areal processes, and the reduction of a high-dimensional parameter space using a novel parameter model. VL - 9 IS - 4 ER - TY - JOUR T1 - Regionalization of Multiscale Spatial Processes using a Criterion for Spatial Aggregation Error JF - ArXiv Y1 - 2015 A1 - Bradley, J. R. A1 - Wikle, C.K. A1 - Holan, S. H. AB - The modifiable areal unit problem and the ecological fallacy are known problems that occur when modeling multiscale spatial processes. We investigate how these forms of spatial aggregation error can guide a regionalization over a spatial domain of interest. By "regionalization" we mean a specification of geographies that define the spatial support for areal data. This topic has been studied vigorously by geographers, but has been given less attention by spatial statisticians. Thus, we propose a criterion for spatial aggregation error (CAGE), which we minimize to obtain an optimal regionalization. To define CAGE we draw a connection between spatial aggregation error and a new multiscale representation of the Karhunen-Loeve (K-L) expansion. This relationship between CAGE and the multiscale K-L expansion leads to illuminating theoretical developments including: connections between spatial aggregation error, squared prediction error, spatial variance, and a novel extension of Obled-Creutin eigenfunctions. The effectiveness of our approach is demonstrated through an analysis of two datasets, one using the American Community Survey and one related to environmental ocean winds. UR - http://arxiv.org/abs/1502.01974 IS - 1502.01974 ER - TY - JOUR T1 - Small Area Estimation via Multivariate Fay-Herriot Models With Latent Spatial Dependence JF - Australian & New Zealand Journal of Statistics Y1 - 2015 A1 - Porter, A.T. A1 - Wikle, C.K. A1 - Holan, S.H. VL - 57 UR - http://arxiv.org/abs/1310.7211 ER - TY - JOUR T1 - A stochastic bioenergetics model based approach to translating large river flow and temperature in to fish population responses: the pallid sturgeon example JF - Geological Society Y1 - 2015 A1 - Wildhaber, M.L. A1 - Dey, R. A1 - Wikle, C.K. A1 - Anderson, C.J. A1 - Moran, E.H. A1 - Franz, K.J. VL - 408 ER - TY - JOUR T1 - Agent Based Models: Statistical Challenges and Opportunities JF - Statistics Views Y1 - 2014 A1 - Wikle, C.K. PB - Wiley UR - http://www.statisticsviews.com/details/feature/6354691/Agent-Based-Models-Statistical-Challenges-and-Opportunities.html ER - TY - CONF T1 - Ecological Prediction with Nonlinear Multivariate Time-Frequency Functional Data Models T2 - Joint Statistical Meetings 2013 Y1 - 2013 A1 - Wikle, C.K. JF - Joint Statistical Meetings 2013 CY - Montreal, Canada ER - TY - JOUR T1 - Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models JF - Journal of Agricultural, Biological, and Environmental Statistics Y1 - 2013 A1 - Yang, W.H., A1 - Wikle, C.K. A1 - Holan, S.H. A1 - Wildhaber, M.L. VL - 18 UR - http://link.springer.com/article/10.1007/s13253-013-0142-1 ER - TY - JOUR T1 - Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dispersion JF - Journal of Agricultural, Biological, and Environmental Statistics Y1 - 2013 A1 - Wu, G. A1 - Holan, S.H. A1 - Wikle, C.K. CY - Anchorage, Alaska VL - 18 UR - http://link.springer.com/article/10.1007/s13253-013-0141-2 ER - TY - CONF T1 - Nonlinear Dynamic Spatio-Temporal Statistical Models T2 - Southern Regional Council on Statistics Summer Research Conference Y1 - 2013 A1 - Wikle, C.K. JF - Southern Regional Council on Statistics Summer Research Conference ER - TY - ABST T1 - Statistics and the Environment: Overview and Challenges Y1 - 2013 A1 - Wikle, C.K. N1 - Invited Introductory Overview Lecture ER - TY - JOUR T1 - An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models JF - Applied Stochastic Models in Business and Industry Y1 - 2012 A1 - Holan, S. A1 - Yang, W. A1 - Matteson, D. A1 - Wikle, C.K. KW - Bayesian model averaging KW - business cycles KW - empirical orthogonal functions KW - functional data KW - MIDAS KW - spectrogram KW - stochastic search variable selection VL - 28 UR - http://onlinelibrary.wiley.com/doi/10.1002/asmb.1954/full N1 - DOI: 10.1002/asmb.1954 ER - TY - CONF T1 - Change of Support in Spatio-Temporal Dynamical Models T2 - Joint Statistical Meetings Y1 - 2012 A1 - Wikle, C.K. JF - Joint Statistical Meetings CY - Montreal, Canada ER - TY - ABST T1 - Efficient Time-Frequency Representations in High-Dimensional Spatial and Spatio-Temporal Models Y1 - 2012 A1 - Wikle, C.K. ER - TY - CONF T1 - Hierarchical General Quadratic Nonlinear Models for Spatio-Temporal Dynamics T2 - Red Raider Conference Y1 - 2012 A1 - Wikle, C.K. JF - Red Raider Conference PB - Texas Tech University CY - Lubbock, TX ER - TY - CHAP T1 - Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal Data T2 - Spatio-temporal Design: Advances in Efficient Data Acquisition Y1 - 2012 A1 - Holan, S. A1 - Wikle, C.K. ED - Jorge Mateu ED - Werner Muller JF - Spatio-temporal Design: Advances in Efficient Data Acquisition PB - Wiley CY - Chichester, UK UR - http://onlinelibrary.wiley.com/doi/10.1002/9781118441862.ch12/summary ER - TY - ABST T1 - Spatio-Temporal Statistics at Mizzou, Truman School of Public Affairs Y1 - 2012 A1 - Wikle, C.K. ER - TY - JOUR T1 - An ensemble quadratic echo state network for nonlinear spatio-temporal forecasting JF - Stat Y1 - 0 A1 - McDermott, P.L. A1 - Wikle, C.K. AB - Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include interactions across multiple scales of spatial and temporal variability. The data sets associated with many of these processes are increasing in size due to advances in automated data measurement, management, and numerical simulator output. Non- linear spatio-temporal models have only recently seen interest in statistics, but there are many classes of such models in the engineering and geophysical sciences. Tradi- tionally, these models are more heuristic than those that have been presented in the statistics literature, but are often intuitive and quite efficient computationally. We show here that with fairly simple, but important, enhancements, the echo state net- work (ESN) machine learning approach can be used to generate long-lead forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty quantification, and at only a fraction of the computational expense of a traditional parametric nonlinear spatio-temporal models. UR - https://arxiv.org/abs/1708.05094 ER -