"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."
"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."Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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