Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Time-series analysis --- Mathematical statistics --- Kalman filtering --- Kalman, filtrage de --- Série chronologique --- Time-series analysis. --- Kalman filtering. --- Filtering, Kalman --- Analysis of time series --- Control theory --- Estimation theory --- Prediction theory --- Stochastic processes --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- 304.5 --- 305.974 --- AA / International- internationaal --- Techniek van de statistische-econometrische voorspellingen. Prognose in de econometrie --- Time varying coefficients. Kalman Filter --- Séries chronologiques --- Economic forecasting --- Prévision économique --- Time series analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Wavelet methods have recently undergone a rapid period of development with important implications for a number of disciplines including statistics. This book has three main objectives: (i) providing an introduction to wavelets and their uses in statistics; (ii) acting as a quick and broad reference to many developments in the area; (iii) interspersing R code that enables the reader to learn the methods, to carry out their own analyses, and further develop their own ideas. The book code is designed to work with the freeware R package WaveThresh4, but the book can be read independently of R. The book introduces the wavelet transform by starting with the simple Haar wavelet transform, and then builds to consider more general wavelets, complex-valued wavelets, non-decimated transforms, multidimensional wavelets, multiple wavelets, wavelet packets, boundary handling, and initialization. Later chapters consider a variety of wavelet-based nonparametric regression methods for different noise models and designs including density estimation, hazard rate estimation, and inverse problems; the use of wavelets for stationary and non-stationary time series analysis; and how wavelets might be used for variance estimation and intensity estimation for non-Gaussian sequences. The book is aimed both at Masters/Ph.D. students in a numerate discipline (such as statistics, mathematics, economics, engineering, computer science, and physics) and postdoctoral researchers/users interested in statistical wavelet methods. Guy Nason is Professor of Statistics at the University of Bristol. He has been actively involved in the development of various wavelet methods in statistics since 1993. He was awarded the Royal Statistical Society’s 2001 Guy Medal in Bronze for work on wavelets in statistics. He was the author of the first, free, generally available wavelet package for statistical purposes in S and R (WaveThresh2).

Programming --- Mathematical statistics --- Statistics. --- Statistical Theory and Methods. --- Econometrics. --- Bioinformatics. --- Psychometrics. --- Data Mining and Knowledge Discovery. --- Data mining. --- Mathematical statistics. --- Statistique --- Exploration de données (Informatique) --- Bio-informatique --- Statistique mathématique --- Econométrie --- Psychométrie --- Wavelets (Mathematics) --- R (Computer program language) --- Statistics --- Data mining --- Bioinformatics --- Econometrics --- Psychometrics --- R (Computer program language). --- Wavelets (Mathematics). --- Civil & Environmental Engineering --- Mathematics --- Physical Sciences & Mathematics --- Engineering & Applied Sciences --- Operations Research --- Mathematical Statistics --- Logiciel --- Computer software --- Méthode statistique --- Statistical methods --- Modèle mathématique --- Mathematical models --- Traitement des données --- Data processing --- Analyse de séries chronologiques --- Time series analysis --- GNU-S (Computer program language) --- Statistical inference --- Statistics, Mathematical --- Wavelet analysis --- Distribution (Probability theory. --- Probability Theory and Stochastic Processes. --- Probabilities --- Sampling (Statistics) --- Domain-specific programming languages --- Harmonic analysis --- Economics, Mathematical --- Distribution functions --- Frequency distribution --- Characteristic functions --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Measurement --- Scaling --- Methodology --- Probabilities. --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Probability --- Combinations --- Chance --- Least squares --- Risk