Asymptotic Properties of Parameter Estimates for Strongly Dependent Random Variables PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Asymptotic Properties of Parameter Estimates for Strongly Dependent Random Variables PDF full book. Access full book title Asymptotic Properties of Parameter Estimates for Strongly Dependent Random Variables by Robert William Fox. Download full books in PDF and EPUB format.
Author: Robert William Fox
Publisher:
ISBN:
Category : Forms, Quadratic
Languages : en
Pages : 392
Get Book Here
Book Description
Author: Robert William Fox
Publisher:
ISBN:
Category : Forms, Quadratic
Languages : en
Pages : 392
Get Book Here
Book Description
Author: Jeffrey M. Wooldridge
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 544
Get Book Here
Book Description
Author: Offer Lieberman
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 34
Get Book Here
Book Description
Author: Masanobu Taniguchi
Publisher: Springer Science & Business Media
ISBN: 146121162X
Category : Mathematics
Languages : en
Pages : 671
Get Book Here
Book Description
The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.
Author: Xiaohong Chen
Publisher:
ISBN:
Category : Hilbert space
Languages : en
Pages : 376
Get Book Here
Book Description
Author: Jérome Dedecker
Publisher: Springer Science & Business Media
ISBN: 038769952X
Category : Mathematics
Languages : en
Pages : 326
Get Book Here
Book Description
This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.
Author: Patrice Bertail
Publisher: Springer Science & Business Media
ISBN: 038736062X
Category : Mathematics
Languages : en
Pages : 491
Get Book Here
Book Description
This book gives an account of recent developments in the field of probability and statistics for dependent data. It covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. There is a section on statistical estimation problems and specific applications. The book is written as a succession of papers by field specialists, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field.
Author: Kameo Matusita
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3112318862
Category : Mathematics
Languages : en
Pages : 580
Get Book Here
Book Description
No detailed description available for "Statistical Sciences and Data Analysis".
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 556
Get Book Here
Book Description
The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).
Author: Bernt P. Stigum
Publisher:
ISBN:
Category : Asymptotic distribution (Probability theory)
Languages : en
Pages : 40
Get Book Here
Book Description
