Author: Guangrui ZhuPublisher:
ISBN:
Category :
Languages : en
Pages : 196
Book Description
Least Squares Estimation and Adaptive Prediction in Non-linear Stochastic Regression Models with Applications to Time Series and Stochastic Systems
Author: Guangrui ZhuElements of Nonlinear Time Series Analysis and Forecasting
Author: Jan G. De GooijerPublisher: Springer
ISBN: 3319432524
Category : Mathematics
Languages : en
Pages : 626
Book Description
This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.
Partially Linear Models
Author: Wolfgang HärdlePublisher: Springer Science & Business Media
ISBN: 3642577008
Category : Mathematics
Languages : en
Pages : 210
Book Description
In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.
Stochastic Theory and Adaptive Control
Author: T. E. DuncanPublisher: Springer
ISBN:
Category : Mathematics
Languages : en
Pages : 526
Book Description
This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics are gien.
Stochastic Neural Networks and Their Applications to Regression Analysis and Time Series Forecasting
Author: Samuel Po-Shing WongPublisher:
ISBN:
Category :
Languages : en
Pages : 168
Book Description
Dissertation Abstracts International
Author: Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 680
Book Description
System-Theoretic Methods in Economic Modelling I
Author: S. MittnikPublisher: Elsevier
ISBN: 1483296229
Category : Business & Economics
Languages : en
Pages : 194
Book Description
The value of applying system-theoretic concepts to economic modelling problems arises from the fact that it offers a unifying framework for modelling dynamic systems. In addition to offering this powerful conceptual framework, it provides a wide range of tools useful in applied work. System-theoretic techniques enter predominantly two stages of economic modelling efforts: the stage of model construction and the stage of model application in accordance with the modelling. The objective of this and subsequent volumes on System-Theoretic Methods in Economic Modelling I is to initiate and/or intensify dialogues between researchers and practitioners within and across the disciplines involved. This first volume brings together papers exhibiting a wide range of system-theoretic techniques and applications to economic problems. The papers have been divided into two groups, following roughly--but not necessarily--the above classification into the construction and application stages of economic modelling. The papers in the first group focus on the identification of dynamic and static systems, while the papers in the second group address dynamic optimization problems.
American Doctoral Dissertations
Author: Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 724
Book Description
University of Michigan Official Publication
Author: University of MichiganPublisher: UM Libraries
ISBN:
Category : Education, Higher
Languages : en
Pages : 212
Book Description
Each number is the catalogue of a specific school or college of the University.
Some Properties of the Least Squares Estimator in Regression Analysis when the Independent Variables are Stochastic
Author: P. K. Bhattacharya (Mathematician)Publisher:
ISBN:
Category : Matrices
Languages : en
Pages : 32
Book Description
For the linear regression of y on x observations the loss in estimating the true regression function by another function is considered as a loss function. For the loss function, it is shown under certain conditions that if the class of estimates which are linear in y's and have bounded risk is non-empty, then the estimate obtained by the method of least squares belongs to this class and has uniformly minimum risk in this class. A necessary and sufficient condition on the distribution function of x observations is obtained for this class to be non-empty, which unfortunately is not easy to verify in particular cases and is violated in a ver simple situation. owever, by a sequential modification of the sampling scheme, this condition may always be satisfied at the cost of an arbitrarily small increase in the expected sa ple size. I T IS ALSO SHOWN UNDER CERTAIN FURTHER C NDITIONS ON THE FAMILY OF ADMISSIBLE DISTRIB TIONS THAT THE LEAST SQUARES ESTIMATOR IS MINIMAX IN THE CLASS OF ALL ESTIMATORS. (Author).