Asymptotic statistics: proceedings of the fifth Prague symposium, held from September 4-9, 1993
The papers collected in this book cover a wide range of topics in asymptotic statistics. In particular up-to-date-information is presented in detection of systematic changes, in series of observation, in robust regression analysis, in numerical empirical processes and in related areas of actuarial sciences and mathematical programming.
The emphasis is on theoretical contributions with impact on statistical methods employed in the analysis of experiments and observations by biometricians, econometricians and engineers.
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Antoch J and Huskova M Procedures for the detection of multiple
Artstein Z Probing for information in twostage stochastic program
Atkinson A C Koopman S J and Sheppard N Outliers and switches
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absolutely continuous approximation assume assumptions asymptotic normality bandwidth bias bootstrap bounded central limit central limit theorem compact components consider consistent constant continuous convex Corollary corresponding defined definition denote density derivative distribution function empirical equivalent equivariance exists finite fix-point follows Frechet differentiable given Gutenbrunner hence holds implies independent inequality integral iteration Jureckova kernel Koenker large numbers law of large Lemma likelihood linear models lower semicontinuous M-estimators Math Mathematics matrix method minimax Moreover nonparametric normal distribution Note observations obtain optimal paper parameter probability measure probability space problem proof of Theorem properties Proposition prove quantile function random variables regression model regression quantiles regression rank scores Remark respect robust estimation Robust Statistics sample satisfying score function Section sequence space Stein stochastic programming subset Test statistics Theorem 3.1 values variance vector zero