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Asymptotic Theory of Statistical Inference for Time Series
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Elements of Stochastic Processes
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Chapter 2
Local Asymptotic Normality for Stochastic Processes
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Chapter 3
Asymptotic Theory of Estimation and Testing for Stochastic Processes
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Chapter 4
Higher Order Asymptotic Theory for Stochastic Processes
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Chapter 5
Asymptotic Theory for Long-Memory Processes
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Chapter 6
Statistical Analysis Based on Functionals of Spectra
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Chapter 7
Discriminant Analysis for Stationary Time Series
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Chapter 8
Large Deviation Theory and Saddlepoint Approximation for Stochastic Processes
Overall attention for this book and its chapters
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Mentioned by
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syllabi
1
institution with syllabi
Citations
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351
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Readers on
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3
Mendeley
Book overview
1. Elements of Stochastic Processes
2. Local Asymptotic Normality for Stochastic Processes
3. Asymptotic Theory of Estimation and Testing for Stochastic Processes
4. Higher Order Asymptotic Theory for Stochastic Processes
5. Asymptotic Theory for Long-Memory Processes
6. Statistical Analysis Based on Functionals of Spectra
7. Discriminant Analysis for Stationary Time Series
8. Large Deviation Theory and Saddlepoint Approximation for Stochastic Processes
Summary
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Syllabi
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This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
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syllabus from an institution on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
Yale University
1
Unknown