TY - JOUR T1 - Bayesian Hierarchical Multi-Population Multistate Jolly-Seber Models with Covariates: Application to the Pallid Sturgeon Population Assessment Program JF - Journal of the American Statistical Association Y1 - 2017 A1 - Wu, G. A1 - Holan, S.H. AB - Estimating abundance for multiple populations is of fundamental importance to many ecological monitoring programs. Equally important is quantifying the spatial distribution and characterizing the migratory behavior of target populations within the study domain. To achieve these goals, we propose a Bayesian hierarchical multi-population multistate Jolly–Seber model that incorporates covariates. The model is proposed using a state-space framework and has several distinct advantages. First, multiple populations within the same study area can be modeled simultaneously. As a consequence, it is possible to achieve improved parameter estimation by “borrowing strength” across different populations. In many cases, such as our motivating example involving endangered species, this borrowing of strength is crucial, as there is relatively less information for one of the populations under consideration. Second, in addition to accommodating covariate information, we develop a computationally efficient Markov chain Monte Carlo algorithm that requires no tuning. Importantly, the model we propose allows us to draw inference on each population as well as on multiple populations simultaneously. Finally, we demonstrate the effectiveness of our method through a motivating example of estimating the spatial distribution and migration of hatchery and wild populations of the endangered pallid sturgeon (Scaphirhynchus albus), using data from the Pallid Sturgeon Population Assessment Program on the Lower Missouri River. Supplementary materials for this article are available online. VL - 112 UR - http://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1211531 IS - 518 ER - TY - JOUR T1 - The Cepstral Model for Multivariate Time Series: The Vector Exponential Model JF - Statistica Sinica Y1 - 2017 A1 - Holan, S.H. A1 - McElroy, T.S. A1 - Wu, G. KW - Autocovariance matrix KW - Bayesian estimation KW - Cepstral KW - Coherence KW - Spectral density matrix KW - stochastic search variable selection KW - Wold coefficients. AB - Vector autoregressive (VAR) models have become a staple in the analysis of multivariate time series and are formulated in the time domain as difference equations, with an implied covariance structure. In many contexts, it is desirable to work with a stable, or at least stationary, representation. To fit such models, one must impose restrictions on the coefficient matrices to ensure that certain determinants are nonzero; which, except in special cases, may prove burdensome. To circumvent these difficulties, we propose a flexible frequency domain model expressed in terms of the spectral density matrix. Specifically, this paper treats the modeling of covariance stationary vector-valued (i.e., multivariate) time series via an extension of the exponential model for the spectrum of a scalar time series. We discuss the modeling advantages of the vector exponential model and its computational facets, such as how to obtain Wold coefficients from given cepstral coefficients. Finally, we demonstrate the utility of our approach through simulation as well as two illustrative data examples focusing on multi-step ahead forecasting and estimation of squared coherence. VL - 27 UR - http://www3.stat.sinica.edu.tw/statistica/J27N1/J27N12/J27N12.html 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 - RPRT T1 - The Cepstral Model for Multivariate Time Series: The Vector Exponential Model. Y1 - 2014 A1 - Holan, S.H. A1 - McElroy, T.S. A1 - Wu, G. AB -

Vector autoregressive (VAR) models have become a staple in the analysis of multivariate time series and are formulated in the time domain as difference equations, with an implied covariance structure. In many contexts, it is desirable to work with a stable, or at least stationary, representation. To fit such models, one must impose restrictions on the coefficient matrices to ensure that certain determinants are nonzero; which, except in special cases, may prove burdensome. To circumvent these difficulties, we propose a flexible frequency domain model expressed in terms of the spectral density matrix. Specifically, this paper treats the modeling of covariance stationary vector-valued (i.e., multivariate) time series via an extension of the exponential model for the spectrum of a scalar time series. We discuss the modeling advantages of the vector exponential model and its computational facets, such as how to obtain Wold coefficients from given cepstral coefficients. Finally, we demonstrate the utility of our approach through simulation as well as two illustrative data examples focusing on multi-step ahead forecasting and estimation of squared coherence.

PB - arXiv UR - http://arxiv.org/abs/1406.0801 ER - TY - CONF T1 - Bayesian Modeling in the Era of Big Data: the Role of High-Throughput and High-Performance Computing T2 - The Extreme Science and Engineering Discovery Environment Conference Y1 - 2013 A1 - Wu, G. JF - The Extreme Science and Engineering Discovery Environment Conference CY - San Diego, CA ER - TY - CONF T1 - Binomial Mixture Models for Urban Ecological Monitoring Studies Using American Community Survey Demographic Covariates T2 - Joint Statistical Meetings 2013 Y1 - 2013 A1 - Wu, G. JF - Joint Statistical Meetings 2013 CY - Montreal, Canada 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 -