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This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics. - ;Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are n

State-space methods. --- Time-series analysis. --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- System analysis

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This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It attempts to bridge the gap between methods and realistic applications. This book contains the most important approaches to analyse time series which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series Granger causality tests and vector autoregressive models are presented. For real applied work the modelling of nonstationary uni- or multivariate time series is most important.

Time-series analysis. --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- Econometrics. --- Statistics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Economics, Mathematical --- Statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics .

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Time-series analysis --- Chaotic behavior in systems --- Fractals --- Série chronologique --- Chaos --- Fractales --- Chaotic behavior in systems. --- Fractals. --- Time-series analysis. --- Série chronologique --- Fractal geometry --- Fractal sets --- Geometry, Fractal --- Sets, Fractal --- Sets of fractional dimension --- Dimension theory (Topology) --- Chaos in systems --- Chaos theory --- Chaotic motion in systems --- Differentiable dynamical systems --- Dynamics --- Nonlinear theories --- System theory --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities

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The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Accompanying lab manual in R

Environmental sciences --- Ecology --- Mathematical models. --- Mathematical models. --- Dirichlet distribution. --- Fisher Information. --- Hadamard product. --- Poisson. --- Weibull distribution. --- autocorrelation. --- autocovariance. --- beta distribution. --- beta-binomial. --- binomial distribution. --- completing the square. --- confidence interval. --- correlation. --- covariance. --- differential equation. --- eigenanalysis. --- exponential distribution. --- extreme value distribution. --- fecundity. --- frequentist. --- gamma distribution. --- generation time. --- integrated analysis. --- inverse gamma. --- kriging. --- logistic population growth. --- longitudinal model. --- multinomial. --- negative binomial. --- positive definite matrix. --- predictive loss. --- spectral density. --- stage structured model. --- uniform distribution.

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A wide variety of processes occur on multiple scales, either naturally or as a consequence of measurement. This book contains methodology for the analysis of data that arise from such multiscale processes. The book brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. The Bayesian approach also facilitates the use of knowledge from prior experience or data, and these methods can handle different amounts of prior knowledge at different scales, as often occurs in practice. The book is aimed at statisticians, applied mathematicians, and engineers working on problems dealing with multiscale processes in time and/or space, such as in engineering, finance, and environmetrics. The book will also be of interest to those working on multiscale computation research. The main prerequisites are knowledge of Bayesian statistics and basic Markov chain Monte Carlo methods. A number of real-world examples are thoroughly analyzed in order to demonstrate the methods and to assist the readers in applying these methods to their own work. To further assist readers, the authors are making source code (for R) available for many of the basic methods discussed herein. Marco A. R. Ferreira is an Assistant Professor of Statistics at the University of Missouri, Columbia. Herbert K. H. Lee is an Associate Professor of Applied Mathematics and Statistics at the University of California, Santa Cruz, and authored the book Bayesian Nonparametrics via Neural Networks.

Bayesian statistical decision theory. --- Time-series analysis. --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Mathematical statistics. --- Distribution (Probability theory. --- Econometrics. --- Computer simulation. --- Computer vision. --- Geography. --- Statistical Theory and Methods. --- Probability Theory and Stochastic Processes. --- Simulation and Modeling. --- Image Processing and Computer Vision. --- Geography, general. --- Cosmography --- Earth sciences --- World history --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Economics, Mathematical --- Statistics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Probabilities. --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Optical equipment