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Advances in Time Series Methods and Applications

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Table of Contents

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    Book Overview
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    Chapter 1 Ian McLeod’s Contribution to Time Series Analysis—A Tribute
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    Chapter 2 The Doubly Adaptive LASSO for Vector Autoregressive Models
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    Chapter 3 On Diagnostic Checking Autoregressive Conditional Duration Models with Wavelet-Based Spectral Density Estimators
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    Chapter 4 Diagnostic Checking for Weibull Autoregressive Conditional Duration Models
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    Chapter 5 Diagnostic Checking for Partially Nonstationary Multivariate ARMA Models
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    Chapter 6 The Portmanteau Tests and the LM Test for ARMA Models with Uncorrelated Errors
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    Chapter 7 Generalized \(C(\alpha )\) Tests for Estimating Functions with Serial Dependence
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    Chapter 8 Regression Models for Ordinal Categorical Time Series Data
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    Chapter 9 Identification of Threshold Autoregressive Moving Average Models
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    Chapter 10 Improved Seasonal Mann–Kendall Tests for Trend Analysis in Water Resources Time Series
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    Chapter 11 A Brief Derivation of the Asymptotic Distribution of Pearson’s Statistic and an Accurate Approximation to Its Exact Distribution
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    Chapter 12 Business Resilience During Power Shortages: A Power Saving Rate Measured by Power Consumption Time Series in Industrial Sector Before and After the Great East Japan Earthquake in 2011
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    Chapter 13 Atmospheric \(\hbox {CO}_2\) and Global Temperatures: The Strength and Nature of Their Dependence
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    Chapter 14 Catching Uncertainty of Wind: A Blend of Sieve Bootstrap and Regime Switching Models for Probabilistic Short-Term Forecasting of Wind Speed
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Title
Advances in Time Series Methods and Applications
Published by
Springer New York, January 2016
DOI 10.1007/978-1-4939-6568-7
ISBNs
978-1-4939-6567-0, 978-1-4939-6568-7
Editors

Wai Keung Li, David A. Stanford, Hao Yu

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 42%
Student > Master 5 26%
Student > Postgraduate 2 11%
Student > Bachelor 1 5%
Student > Doctoral Student 1 5%
Other 2 11%
Readers by discipline Count As %
Environmental Science 4 21%
Computer Science 4 21%
Engineering 3 16%
Agricultural and Biological Sciences 2 11%
Unspecified 2 11%
Other 4 21%