Buch, Englisch, Band 38, 368 Seiten, Format (B × H): 164 mm x 250 mm, Gewicht: 674 g
Second Edition
Buch, Englisch, Band 38, 368 Seiten, Format (B × H): 164 mm x 250 mm, Gewicht: 674 g
Reihe: Oxford Statistical Science Series
ISBN: 978-0-19-964117-8
Verlag: Sydney University Press
problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.
Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.
Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.
Zielgruppe
Researchers in statistics, econometrics, biometrics, environmetrics, engineering, system theory and physics. Financial analysts in banking and other financial institutions.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
Weitere Infos & Material
1: Introduction
Part I: The linear state space model
2: Local level model
3: Linear Gaussian state space models
4: Filtering, smoothing and forecasting
5: Initialisation of Filter and smoother
6: Further computational aspects
7: Maximum likelihood estimation of parameters
8: Illustrations of the use of the linear Gaussian model
Part II: Non-Gaussian and nonlinear state space models
9: Special cases of nonlinear and non-Gaussian models
10: Approximate filtering and smoothing
11: Importance sampling for smoothing
12: Particle filtering
13: Bayesian estimation of parameters
14: Non-Gaussian and nonlinear illustrations
Subject Index