Download or read book Reparametrized Dynamic Space Time Models and Spatial Model Selection written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in diverse areas such as environmental and health sciences are increasingly facing working with space-time data. Often the dimension of space-time data sets can be very large and moreover, space-time processes are often complicated in that the dependence structure across space and time is non-trivial, often non-separable and non-stationary in space and/or time. Hence, space-time modeling is a challenging task and in particular parameter estimation can be problematic due to the high dimensionality. We propose a reparametrization approach to fit dynamic space-time models with an unstructured covariance function. Our modeling contribution is to present unconstrained reparametrization for a covariance matrix in dynamic space-time models. Using this unconstrained reparametrization method, we are able to implement the modeling of a high dimensional covariance matrix that automatically maintains the positive definiteness constraint. We illustrate the use of this reparametrization method by applying our model to a set of atmospheric nitrate concentration data. We also consider the problem of model selection for spatial data. The issue of model selection in spatial models has rarely been addressed in the literature, though it is very important. To address this problem, we consider selection criteria such as the Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC). The performance of these selection criteria are examined using Monte Carlo simulations. In particular, the ability of these criteria to select the correct model is evaluated.

Download or read book Reparametrized Dynamic Space time Models and Spatial Model Selection written by Hyeyoung Lee and published by . This book was released on 2006 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: total nitrate concentration, information criteria, dynamic linear models.

Download or read book Spatial Dynamics and Optimal Space time Development written by Walter Isard and published by North Holland. This book was released on 1979 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book State Space Modeling of Time Series written by Masanao Aoki and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.

Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski and published by CRC Press. This book was released on 2018-12-07 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Download or read book Transformations Through Space and Time written by Daniel A. Griffith and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there has been a growing concern for the development of both efficient and effective ways to handle space-time problems. Such developments should be theoretically as well as empirically oriented. Regardless of which of these two arenas one enters. the impression is quickly gained that contemporary wO,rk on dynamic and evolutionary models has not proved to be as illuminating and rewarding as first anticipated. Historically speaking. the single. most important lesson this avenue of research has provided. is that linear models are woefully inadequate when dominant non-linear trends and relationships prevail. and that independent activities and actions are all but non-existent in the real-world. Meanwhile. one prominent imp 1 ication stemming from this 1 iterature is that the easiest modelling tasks are those of specifying good dynamic space-time models. Somewhat more problematic are the statistical questions of model specification. parameter estimation. and model validation. whereas even more problematic is the operationalization of evolutionary conceptual models. A timely next step in spatial analysis would seem to be a return to basics. with a pronounced focus both on specific problems (and data) and on the mechanisms that transform phenomena through space and/or time'. It appears that these transformation mechanisms must embrace both non-linear and autoregressive formalisms. Given. also. the variety of geographic forms. they must allow for bifurcation points to emerge. too.

Download or read book Modern Methodology and Applications in Spatial Temporal Modeling written by Gareth William Peters and published by Springer. This book was released on 2016-01-08 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​ This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.

Download or read book Spatial Econometrics written by J. Paul Elhorst and published by Springer Science & Business Media. This book was released on 2013-09-30 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted.

Download or read book Physics Based Dynamic Modeling of Space time Data written by Fabio Roman Albert Sigrist and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Parameterization and Estimation of Spatio temporal Dynamic Models written by (Bill) Ke Xu and published by . This book was released on 2004 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of two separate but related topics in spatio-temporal modeling. In the first part we investigate the problem of GEM (General EM) estimation of spatio-temporal models. We propose a spatio-temporal dynamic model formulation with parameter matrices further specified based on science and/or common spatial models. Several parame-terization strategies are proposed and analytical or computational closed form GEM update equations are derived for each. We apply the methodology to a diffusion space-time model in a simulation study and also apply a dimension-reduced model to a U.S. Palmer Drought Severity Index (PDSI) data set. In the second part we look at a convolution kernel approach to modeling diffusive propagation in the framework of Bayesian hierarchical spatio-temporal dynamic models. The diffusive propagation is modeled through the displacement of the convolution kernel. Computation efficiency is achieved by using the FFT and spectral dimension reduction. The method is applied to a thunderstorm nowcasting problem in Sydney, Australia.

Download or read book Lagoon Environments Around the World written by Andrew James Manning and published by BoD – Books on Demand. This book was released on 2020-03-11 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lagoon Environments Around the World - A Scientific Perspective covers a wide range of topics. Typically bordering between land and sea, lagoons are among the most diversely utilized waterways on the planet. Lagoons are extremely important environments socio-economically, and their usage places ever increasing stress on these very sensitive aquatic regions. The effective management of shallow aquatic environments requires a detailed scientific understanding of the various contributary natural processes. This has both environmental and economic implications, especially where there is any anthropogenic involvement. This book draws on international scientific research to examine the following lagoon related issues: classification, circulation hydrodynamics, ecosystems, sedimentation, anthropogenic stresses, and response to extreme events. The research was carried out by researchers who specialize in shallow water processes and related issues.

Download or read book Modeling for Spatial and Spatio temporal Data with Applications written by Xintong Li and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: It is common to assume the spatial or spatio-temporal data are realizations of underlying random fields or stochastic processes. Effective approaches to modeling of the underlying autocorrelation structure of the same random field and the association among multiple processes are of great demand in many areas including atmospheric sciences, meteorology and agriculture. To this end, this dissertation studies methods and application of the spatial modeling of large-scale dependence structure and spatio-temporal regression modelling. First, variogram and variogram matrix functions play important roles in modeling dependence structure among processes at different locations in spatial statistics. With more and more data collected on a global scale in environmental science, geophysics, and related fields, we focus on the characterizations of the variogram models on spheres of all dimensions for both stationary and intrinsic stationary, univariate and multivariate random fields. Some effcient approaches are proposed to construct a variety of variograms including simple polynomial structures. In particular, the series representation and spherical behavior of intrinsic stationary random fields are explored in both theoretical and simulation study. The applications of the proposed model and related theoretical results are demonstrated using simulation and real data analysis. Second, knowledge of the influential factors on the number of days suitable for fieldwork (DSFW) has important implications on timing of agricultural field operations, machinery decision, and risk management. To assess how some global climate phenomena such as El Nino Southern Oscillation (ENSO) affects DSFW and capture their complex associations in space and time, we propose various spatio-temporal dynamic models under hierarchical Bayesian framework. The Integrated Nested Laplace Approximation (INLA) is used and adapted to reduce the computational burden experienced when a large number of geo-locations and time points is considered in the data set. A comparison study between dynamics models with INLA viewing spatial domain as discrete and continuous is conducted and their pros and cons are evaluated based on multiple criteria. Finally a model with time- varying coefficients is shown to reflect the dynamic nature of the impact and lagged effect of ENSO on DSFW in US with spatio-temporal correlations accounted.

Download or read book The Effects of Spatial Aggregation on Spatial Time Series Modeling and Forecasting written by Andrew J. Gehman and published by . This book was released on 2016 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatio-temporal data analysis involves modeling a variable observed at different locations over time. A key component of space-time modeling is determining the spatial scale of the data. This dissertation addresses the following three questions: 1) How does spatial aggregation impact the properties of the variable and its model? 2) What spatial scale of the data produces more accurate forecasts of the aggregate variable? 3) What properties lead to the smallest information loss due to spatial aggregation? Answers to these questions involve a thorough examination of two common space-time models: the STARMA and GSTARMA models. These results are helpful to researchers seeking to understand the impact of spatial aggregation on temporal and spatial correlation as well as to modelers interested in determining a spatial scale for the data. Two data examples are included to illustrate the findings, and they concern states' annual labor force totals and monthly burglary counts for police districts in the city of Philadelphia.

Download or read book Dynamic Spatial Autoregressive Models with Time Varying Spatial Weighting Matrices written by Anna Gloria Billé and published by . This book was released on 2020 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new spatio--temporal model with time--varying spatial weighting matrices. The filtering procedure of the time--varying unknown parameters is performed using the information contained in the score of the conditional distribution of the observables. We provide conditions for the stationarity and ergodicity of the filtered sequence of the spatial matrices as well as for the consistency and asymptotic normality of the maximum likelihood estimator (MLE). An extensive Monte Carlo simulation study to investigate the finite sample properties of the maximum likelihood estimator is also reported. We finally analyze the association between eight European countries' perceived risk, suggesting that the economically strong countries have their perceived risk increased due to their spatial connection with the economically weaker countries. We also investigate the evolution of the spatial connection between the house prices in different areas of the UK, identifying periods when the usually adopted sparse weighting matrix is not sufficient to describe the underlying spatial process.

Download or read book Theory and Application of Dynamic Spatial Time Series Models written by and published by . This book was released on 2020 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.