Vor Ort
Merkzettel Warenkorb
  • Produktbild: Asymptotic Theory of Statistical Inference for Time Series
  • Produktbild: Asymptotic Theory of Statistical Inference for Time Series
- 16%

Asymptotic Theory of Statistical Inference for Time Series

Aus der Reihe Springer Series in Statistics
16% sparen

156,99 € UVP 186,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2012

Abbildungen

XVII, 662 p.

Verlag

Springer Us

Seitenzahl

662

Maße (L/B/H)

23,5/15,5/3,7 cm

Gewicht

1019 g

Auflage

Softcover reprint of the original 1st edition 2000

Sprache

Englisch

ISBN

978-1-4612-7028-7

Beschreibung

Rezension

From the reviews:


MATHEMATICAL REVIEWS


"It is valuable both as an advanced graduate level text and as a reference for researchers?he book can be most strongly recommended."

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2012

Abbildungen

XVII, 662 p.

Verlag

Springer Us

Seitenzahl

662

Maße (L/B/H)

23,5/15,5/3,7 cm

Gewicht

1019 g

Auflage

Softcover reprint of the original 1st edition 2000

Sprache

Englisch

ISBN

978-1-4612-7028-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Asymptotic Theory of Statistical Inference for Time Series
  • Produktbild: Asymptotic Theory of Statistical Inference for Time Series
  • 1 Elements of Stochastic Processes.- 1.1 Introduction.- 1.2 Stochastic Processes.- 1.3 Limit Theorems.- Problems.- 2 Local Asymptotic Normality for Stochastic Processes.- 2.1 General Results for Local Asymptotic Normality.- 2.2 Local Asymptotic Normality for Linear Processes.- Problems.- 3 Asymptotic Theory of Estimation and Testing for Stochastic Processes.- 3.1 Asymptotic Theory of Estimation and Testing for Linear Processes.- 3.2 Asymptotic Theory for Nonlinear Stochastic Models.- 3.3 Asymptotic Theory for Continuous Time Processes.- Problems.- 4 Higher Order Asymptotic Theory for Stochastic Processes.- 4.1 Introduction to Higher Order Asymptotic Theory.- 4.2 Valid Asymptotic Expansions.- 4.3 Higher Order Asymptotic Estimation Theory for Discrete Time Processes in View of Statistical Differential Geometry.- 4.4 Higher Order Asymptotic Theory for Continuous Time Processes.- 4.5 Higher Order Asymptotic Theory for Testing Problems.- 4.6 Higher Order Asymptotic Theory for Normalizing Transformations.- 4.7 Generalization of LeCam’s Third Lemma and Higher Order Asymptotics of Iterative Methods.- Problems.- 5 Asymptotic Theory for Long-Memory Processes.- 5.1 Some Elements of Long-Memory Processes.- 5.2 Limit Theorems for Fundamental Statistics.- 5.3 Estimation and Testing Theory for Long-Memory Processes.- 5.4 Regression Models with Long-Memory Disturbances.- 5.5 Semiparametric Analysis and the LAN Approach.- Problems.- 6 Statistical Analysis Based on Functionals of Spectra.- 6.1 Estimation of Nonlinear Functionals of Spectra.- 6.2 Application to Parameter Estimation for Stationary Processes.- 6.3 Asymptotically Efficient Nonparametric Estimation of Functionals of Spectra in Gaussian Stationary Processes.- 6.4 Robustness in the Frequency Domain Approach.- 6.5 NumericalExamples.- Problems.- 7 Discriminant Analysis for Stationary Time Series.- 7.1 Basic Formulation.- 7.2 Standard Methods for Gaussian Stationary Processes.- 7.3 Discriminant Analysis for Non-Gaussian Linear Processes.- 7.4 Nonparametric Approach for Discriminant Analysis.- 7.5 Parametric Approach for Discriminant Analysis.- 7.6 Derivation of Spectral Expressions to Divergence Measures Between Gaussian Stationary Processes.- 7.7 Miscellany.- Problems.- 8 Large Deviation Theory and Saddlepoint Approximation for Stochastic Processes.- 8.1 Large Deviation Theorem 538 8.2 Asymptotic Efficiency for Gaussian Stationary Processes:Large Deviation Approach.- 8.3 Large Deviation Results for an Ornstein-Uhlenbeck Process.- 8.4 Saddlepoint Approximations for Stochastic Processes.- Problems.- A.1 Mathematics.- A.2 Probability.- A.3 Statistics.