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Produktbild: A Course in Mathematical Statistics and Large Sample Theory
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A Course in Mathematical Statistics and Large Sample Theory

Aus der Reihe Springer Texts in Statistics
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104,99 € UVP 117,69 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.06.2018

Abbildungen

XI, 389 p. 9 illus., 2 illus. in color.

Verlag

Springer Us

Seitenzahl

389

Maße (L/B/H)

25,4/17,8/2,1 cm

Gewicht

848 g

Auflage

Softcover reprint of the original 1st ed. 2016

Sprache

Englisch

ISBN

978-1-4939-8159-5

Beschreibung

Rezension

“It deals with advanced statistical theory with a special focus on statistical inference and large sample theory, aiming to cover the material for a modern two-semester graduate course in mathematical statistics. … Overall, the book is very advanced and is recommended to graduate students with sound statistical backgrounds, as well as to teachers, researchers, and practitioners who wish to acquire more knowledge on mathematical statistics and large sample theory.” (Lefteris Angelis, Computing Reviews, March, 2017)

“This is a very nice book suitable for a theoretical statistics course after having worked through something at the level of Casella & Berger, as well as some measure theory. … In addition to the exercises, which range from doable to interesting, there are several projects scattered throughout the text.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.06.2018

Abbildungen

XI, 389 p. 9 illus., 2 illus. in color.

Verlag

Springer Us

Seitenzahl

389

Maße (L/B/H)

25,4/17,8/2,1 cm

Gewicht

848 g

Auflage

Softcover reprint of the original 1st ed. 2016

Sprache

Englisch

ISBN

978-1-4939-8159-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: A Course in Mathematical Statistics and Large Sample Theory
  • 1 Introduction.- 2 Decision Theory.- 3 Introduction to General Methods of Estimation.- 4 Sufficient Statistics, Exponential Families, and Estimation.- 5 Testing Hypotheses.- 6 Consistency and Asymptotic Distributions and Statistics.- 7 Large Sample Theory of Estimation in Parametric Models.- 8 Tests in Parametric and Nonparametric Models.- 9 The Nonparametric Bootstrap.- 10 Nonparametric Curve Estimation.- 11 Edgeworth Expansions and the Bootstrap.- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces.- 13 Multiple Testing and the False Discovery Rate.- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory.- 15 Miscellaneous Topics.- Appendices.- Solutions of Selected Exercises in Part 1.