A Non-linear, Non-gaussian State-space Approach to Financial Portfolio Risk Analysis Using Particle Filters - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Non-linear, Non-gaussian State-space Approach to Financial Portfolio Risk Analysis Using Particle Filters write by Sean Terry Smith. This book was released on 2006. A Non-linear, Non-gaussian State-space Approach to Financial Portfolio Risk Analysis Using Particle Filters available in PDF, EPUB and Kindle.
Filtering None-Linear State Space Models. Methods and Economic Applications
Filtering None-Linear State Space Models. Methods and Economic Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Filtering None-Linear State Space Models. Methods and Economic Applications write by Kai Ming Lee. This book was released on 2010. Filtering None-Linear State Space Models. Methods and Economic Applications available in PDF, EPUB and Kindle.
State-Space Models
State-Space Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook State-Space Models write by Yong Zeng. This book was released on 2013-08-15. State-Space Models available in PDF, EPUB and Kindle. State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data. The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals.
State Space and Unobserved Component Models
State Space and Unobserved Component Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook State Space and Unobserved Component Models write by James Durbin. This book was released on 2004-06-10. State Space and Unobserved Component Models available in PDF, EPUB and Kindle. A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.
Beyond the Kalman Filter: Particle Filters for Tracking Applications
Beyond the Kalman Filter: Particle Filters for Tracking Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Beyond the Kalman Filter: Particle Filters for Tracking Applications write by Branko Ristic . This book was released on 2003-12-01. Beyond the Kalman Filter: Particle Filters for Tracking Applications available in PDF, EPUB and Kindle. For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
