๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Identification and estimation of discrete state-vector models with stochastic inputs

โœ Scribed by Pieter W. Otter

Publisher
Elsevier Science
Year
1981
Tongue
English
Weight
233 KB
Volume
17
Category
Article
ISSN
0005-1098

No coin nor oath required. For personal study only.

โœฆ Synopsis

The study deals with the identification and estimation of the unknown parameters of an 'extended' statevector model, in which stochastic input variables are treated as 'state'-variables and the observed input-values as 'output'values of the model.

A parameter identifiability criterion, based on Fisher's information matrix, is applied to the model and a general ML-estimation procedure is given. If a certain restriction on the covariance-matrix of the state-vector is placed, the MLprocedure simplifies and coincides with an operational method, called the Lisrel procedure. This procedure provides also a test for parameter identifiability.

๐Ÿ“œ SIMILAR VOLUMES

State estimation of stochastic singularl
State estimation of stochastic singularly perturbed discrete-time systems
โœ Hisashi Kando ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 360 KB ๐Ÿ‘ 1 views

Studies on discrete-time system analysis and design via singular perturbations and time-scale methods have been developed in recent years. Representative issues and results of modelling, analysis and control have been reviewed by Naidu et al. These studies can be classified into the slow-time-scale

State-space stochastic volatility models
State-space stochastic volatility models: A review of estimation algorithms
โœ Capobianco, Enrico ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 904 KB

Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH-type models; but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particu

On-line estimation and identification of
On-line estimation and identification of HMMs with grouped state values
โœ Iain B. Collings; John B. Moore ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 997 KB

This paper presents a signal-processing scheme for the class of lumpable or weakly lumpable hidden Markov models (HMMs) which have state values clustered into groups. Attention is focused not only on state estimation for known models but also on on-line model identification. The approach taken emplo

Numerical Methods and Parameter Estimati
Numerical Methods and Parameter Estimation of a Structured Population Model with Discrete Events in the Life History
โœ F.D.L KELPIN; M.A KIRKILIONIS; B.W KOOI ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 224 KB

We consider two numerical methods for the solution of a physiologically structured population (PSP) model with multiple life stages and discrete event reproduction. The model describes the dynamic behaviour of a predator}prey system consisting of rotifers predating on algae. The nitrate limited alga