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Full Description
"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."
Contents
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.
