Posted by **Underaglassmoon** at May 15, 2016

Springer | Statistics | June 10, 2016 | ISBN-10: 331928598X | 541 pages | pdf | 4.82 mb

Authors: Gómez, Víctor

Refers to a webpage with algorithms programmed in MATLAB and numerous examples

Studies the relationship between VARMA and state space models and between Wiener-Kolmogorov theory and Kalman filtering

Posted by **BUGSY** at June 20, 2015

English | 2001 | ISBN: 0198523548 | 273 Pages | DJVU | 4 MB

This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence terms…

Posted by **arundhati** at Dec. 31, 2013

2012 | ISBN-10: 019964117X | 368 pages | PDF | 2 MB

Posted by **Nice_smile)** at Jan. 13, 2017

English | 2007 | ISBN: 0199228876 | 240 Pages | PDF | 1.05 MB

Posted by **nebulae** at Dec. 14, 2016

English | ISBN: 1439845727 | 2014 | 282 pages | PDF | 16 MB

Posted by **stabiq** at March 15, 2016

1998 | PDF | 571 pages | ISBN-10: 0256246394, 0071160957 | English | 20 MB

This book addresses two primary deficiencies in the linear systems textbook market: a lack of development of state space methods from the basic principles and a lack of pedagogical focus.

Posted by **interes** at Jan. 30, 2015

English | 2007-08-30 | ISBN: 0199228876 | 240 pages | PDF | 1 MB

Posted by **roxul** at Aug. 31, 2014

English | ISBN: 1439845727 | 2014 | 282 pages | PDF | 16 MB

Posted by **ChrisRedfield** at May 24, 2014

Published: 2013-12-27 | ISBN: 1461491312 | PDF | 220 pages | 3 MB

Posted by **interes** at May 14, 2014

English | 2013 | ISBN: 1118617908 | ISBN-13: 9781118617908 | 520 pages | PDF | 5,5 MB

An accessible guide to the multivariate time series tools used in numerous real-world applications

Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series.