Basics of Modern Mathematical Statistics

DOI10.1007/978-3-642-39909-1zbMath1401.62006OpenAlexW1781762476MaRDI QIDQ2842847

Thorsten Dickhaus, Vladimir Spokoiny

Publication date: 16 August 2013

Published in: Springer Texts in Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-39909-1

zbMATH Keywords

maximum likelihood estimationasymptotic propertiesHellinger distanceBayes estimationsemiparametric estimationridge regressionlinear modelsKullback-Leibler divergencelinear regressionmethod of momentsregression estimationM-estimationCramer-Rao inequalityGlivenko-Cantelli theoremNeyman-Pearson testgeneralized regressionquadratic log likelihoodpiecewise methodquasi maximum likelihood approach

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Parametric inference (62Fxx) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01)

Related Items (4)

Alternating least squares in generalized linear models ⋮ Model-free bounds on value-at-risk using extreme value information and statistical distances ⋮ Nonasymptotic one- and two-sample tests in high dimension with unknown covariance structure ⋮ An Introduction to Approximate Bayesian Computation

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