Asymptotics, Nonparametrics, and Time Series (eBook, PDF)

Redaktion: Ghosh, Subir

• Format: PDF

Produktbeschreibung

"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 854
• Erscheinungstermin: 18. Februar 1999
• Englisch
• ISBN-13: 9781482269772
• Artikelnr.: 57589769

Autorenporträt

Subir Ghosh

Inhaltsangabe

Some examples of empirical fourier analysis in scientific problems
modeling and inference for periodically correlated time series
modeling time series of count data
seasonal and cyclical long memory
nonparametric specification procedures for time series
parameter estimation and model selection for multistep prediction of a time series - a review
nonlinear estimation for time series observed on arrays
some contributions to multivariate nonlinear time series and to bilinear models
optimal testing for semiparametric AR models - from Gaussian Lagrange multipliers to autoregression rank scores and adaptive tests
statistical analysis based on functionals of nonparametric spectral density estimators
efficient estimation in a semiparametric additive regression model with ARMA errors
efficient estimation in Markov chain models - an introduction
nonparametric functional estimation - an overview
minimum distance and nonparametric dispersion functions
estimators of changes
on inverse estimation
approaches for semiparametric Bayesian regression
consistency issues in Bayesian nonparametrics
breakdown theory for estimators based on bootstrap and other resampling schemes
on second-order properties of the stationary bootstrap method for studentized statistics
convergence to equilibrium of random dynamical systems generated by IID monotone maps, with applications to economics
chi-squared tests of goodness-of-fit for dependent observations
positive and negative dependence with some statistical applications
second-order information loss due to nuisance parameters - a simple measure. Appendix: publications of Madan Lal Puri.