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A01=Dimitris N. Politis
A01=Tucker S. McElroy
ARMA Process
ARMA time series
asymptotic theory
Author_Dimitris N. Politis
Author_Tucker S. McElroy
Autocovariance Function
bootstrap methods
Category=KCH
Category=PBT
Classical Decomposition
De-trended Series
Differential Entropy
entropy methods
Entropy Rate
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Gaussian AR
Gaussian Random Vector
Gaussian Time Series
geometric approach
Joint PDF
Kl Discrepancy
linear filters
Monthly Time Series
Moving Average
Moving Average Filter
nonlinear time series bootstrap
Partial Autocorrelation
R coding
R programming exercises
Random Vector
Relative Entropy
Sample Paths
Seasonal Filter
Seasonal Moving Average
spectral analysis
Spectral Density
Stationary Gaussian Time Series
Stochastic Trend
Time Invariant Linear Filter
time series modeling
Weak Stationarity

Product details

  • ISBN 9781032083308
  • Weight: 825g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness, (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.S. students. Basic theoretical results are presented in a mathematically convincing way, and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results.

The book provides the foundation of time series methods, including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth, as well as frequency domain methods. Entropy and other information theoretic notions are introduced, with applications to time series modeling. The second half of the book focuses on statistical inference, the fitting of time series models, as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail, but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain, the discussion of entropy maximization, and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises, half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples, as well as the solutions to exercises.

Tucker S. McElroy is Senior Time Series Mathematical Statistician at the U.S. Census Bureau, where he has contributed to developing time series research and software for the last 15 years. He has published more than 80 papers and is a recipient of the Arthur S. Flemming award (2011).

Dimitris N. Politis is Distinguished Professor of Mathematics at the University of California at San Diego, where he is also serving as Associate Director of the Halıcıoğlu Data Science Institute. He has co-authored two research monographs and more than 100 journal papers. He is a recipient of the Tjalling C. Koopmans Econometric Theory Prize (2009-2011) and is Co-Editor of the Journal of Time Series Analysis.

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