Prediction, filtering and smoothing in non-linear and non-normal cases using Monte Carlo integration

✍ Scribed by Tanizaki H., Mariano R.S.

Year: 1994
Tongue: English
Leaves: 18
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

A simulation-based non-linear filter is developed for prediction and smoothing in non-linear and/or non-normal structural time-series models. Recursive algorithms of weighting functions are derived by applying Monte Carlo integration. Through Monte Carlo experiments, it is shown that (1) for a small number of random draws (or nodes) our simulation-based density estimator using Monte Carlo integration (SDE) performs better than Kitagawa's numerical integration procedure (KNI), and (2) SDE and KNI give less biased parameter estimates than the extended Kalman filter (EKF). Finally, an estimation of per capita final consumption data is taken as an application to the non-linear filtering problem.

📜 SIMILAR VOLUMES

Non-linear and non-normal filter based o

📁 Non-linear and non-normal filter based on Monte-Carlo technique

✍ Hisashi Tanizaki 📂 Library 📅 1997 🌐 English

A non-linear and/or non-normal filter is proposed in this paper. Generating random draws of the state vector directly from the filtering density, the filtering estimate is obtained, which gives us a recursive algorithm. There, we do not evaluate any integration included in the density-based filterin

Monte-Carlo methods and stochastic proce

📁 Monte-Carlo methods and stochastic processes: from linear to non-linear

✍ Gobet, Emmanuel 📂 Library 📅 2016 🏛 Chapman & Hall/CRC 🌐 English

Linear Systems: Non-Fragile Control and

📁 Linear Systems: Non-Fragile Control and Filtering

✍ Guang-Hong Yang, Xiang-Gui Guo, Wei-Wei Che, Wei Guan 📂 Library 📅 2013 🏛 CRC Press 🌐 English

Linear Systems: Non-Fragile Control and Filtering presents the latest research results and a systematic approach to designing non-fragile controllers and filters for linear systems. The authors combine the algebraic Riccati technique, the linear matrix inequality (LMI) technique, and the sensitivity

Binary Polynomial Transforms and Non-Lin

📁 Binary Polynomial Transforms and Non-Linear Digital Filters

✍ S. Agaian, Jaakko Astola, Karen Egiazarian 📂 Library 📅 1995 🏛 Chapman and Hall/CRC 🌐 English

This work offers a unified presentation of the theory of binary polynomial transforms and details their numerous applications in nonlinear signal processing. The book also: introduces the Rademacher logical functions; considers fast algorithms for computing Rademacher and polynomial logical function

Stochastic Evolution Systems: Linear The

📁 Stochastic Evolution Systems: Linear Theory and Applications to Non-linear Filtering

✍ B. L. Rozovskii (auth.) 📂 Library 📅 1990 🏛 Springer Netherlands 🌐 English

Stochastic Evolution Systems: Linear The

📁 Stochastic Evolution Systems: Linear Theory and Applications to Non-Linear Filtering

✍ Boris L. Rozovsky, Sergey V. Lototsky 📂 Library 📅 2018 🏛 Springer 🌐 English

This monograph, now in a thoroughly revised second edition, develops the theory of stochastic calculus in Hilbert spaces and applies the results to the study of generalized solutions of stochastic parabolic equations. The emphasis lies on second-order stochastic parabolic equations and their conn