A Course in Mathematical Statistics and Large Sample Theory

DOI10.1007/978-1-4939-4032-5zbMath1358.62002OpenAlexW2514372789MaRDI QIDQ2821704

Lizhen Lin, Victor Patrangenaru, Rabi N. Bhattacharya

Publication date: 22 September 2016

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

Full work available at URL: https://doi.org/10.1007/978-1-4939-4032-5

zbMATH Keywords

bootstrapdecision theorynonparametric bootstrapEdgeworth expansionsnonparametric curve estimationmathematical statisticslarge sample theorytesting hypothesismethods of estimationconsistency and asymptotic distribution of statisticsCornish-Fischer expansionslarge sample theory of estimation in parametric modelsMarkov Chain Monte Carlo simulationssufficient statisicstests in parametric and nonparametric models

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Parametric hypothesis testing (62F03) Nonparametric estimation (62G05) Sampling theory, sample surveys (62D05) Markov processes: estimation; hidden Markov models (62M05) Nonparametric statistical resampling methods (62G09) Monte Carlo methods (65C05) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Discrete-time Markov processes on general state spaces (60J05) Sufficient statistics and fields (62B05) General considerations in statistical decision theory (62C05)

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