Author: Douglas Gardiner SteigerwaldPublisher:
ISBN:
Category :
Languages : en
Pages : 180
Book Description
Adaptive Estimation in Time Series Regression Models
Author: Douglas Gardiner SteigerwaldPublisher:
ISBN:
Category :
Languages : en
Pages : 180
Book Description
Adaptive Estimation of Non-linear Regression Models
Author: Charles F. ManskiPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 51
Book Description
Adaptive Estimation of Regression Models Via Moment Restrictions
Author: Whitney K. NeweyPublisher:
ISBN:
Category : Parameter estimation
Languages : en
Pages : 50
Book Description
Least Squares Estimation and Adaptive Prediction in Non-linear Stochastic Regression Models with Applications to Time Series and Stochastic Systems
Author: Guangrui ZhuRecursive Estimation and Time-Series Analysis
Author: Peter C. YoungPublisher: Springer Science & Business Media
ISBN: 3642219810
Category : Technology & Engineering
Languages : en
Pages : 505
Book Description
This is a revised version of the 1984 book of the same name but considerably modified and enlarged to accommodate the developments in recursive estimation and time series analysis that have occurred over the last quarter century. Also over this time, the CAPTAIN Toolbox for recursive estimation and time series analysis has been developed at Lancaster, for use in the MatlabTM software environment (see Appendix G). Consequently, the present version of the book is able to exploit the many computational routines that are contained in this widely available Toolbox, as well as some of the other routines in MatlabTM and its other toolboxes. The book is an introductory one on the topic of recursive estimation and it demonstrates how this approach to estimation, in its various forms, can be an impressive aid to the modelling of stochastic, dynamic systems. It is intended for undergraduate or Masters students who wish to obtain a grounding in this subject; or for practitioners in industry who may have heard of topics dealt with in this book and, while they want to know more about them, may have been deterred by the rather esoteric nature of some books in this challenging area of study.
Adaptive Regression
Author: Yadolah DodgePublisher: Springer Science & Business Media
ISBN: 1441987665
Category : Mathematics
Languages : en
Pages : 188
Book Description
While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
Adaptive Estimation in Time-series Models
Author: Feike C. DrostPublisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 48
Book Description
Adaptive Estimation of Random Parameter Regression Models by State Space Representation Methods
Author: Kiseok LeePublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 142
Book Description
Adaptive Estimation in Semiparametric Regression Models with Conditionally Heteroskedastic Disturbances
Author: Yanquin FanPublisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 17
Book Description
Multiple Model Adaptive Estimation for Time Series Analysis
Author: Ibrahim DulgerPublisher:
ISBN: 9781423529293
Category : Economic forecasting
Languages : en
Pages : 153
Book Description
Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions. Our Kalman filters have a dynamics model based on a Box-Jenkins Auto-Regressive Moving Average (ARMA) model and a measure model with additive noise. The time-series prediction is based on the probabilistic weighted Kalman filter predictions. We make a probability interval about that estimate also based on the filter probabilities. In a Monte Carlo analysis, we test this MMAE approach and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when the Kalman filter dynamics models did not match the data generation time-series model. Our analysis indicates benefits in applying multiple model adaptive estimation for time series analysis.