Pub. Date:
Elsevier Science
Handbook of Statistics: Time Series Analysis: Methods and Applications

Handbook of Statistics: Time Series Analysis: Methods and Applications

by Elsevier ScienceElsevier Science
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The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience.

  • Comprehensively presents the various aspects of statistical methodology
  • Discusses a wide variety of diverse applications and recent developments
  • Contributors are internationally renowened experts in their respective areas

Product Details

ISBN-13: 9780444538581
Publisher: Elsevier Science
Publication date: 07/27/2012
Series: Handbook of Statistics Series , #30
Pages: 774
Product dimensions: 6.20(w) x 9.10(h) x 1.40(d)

About the Author

Professor C. R. Rao, born in India, is one of this century's foremost statisticians, and received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. He is Emeritus Holder of the Eberly Family Chair in Statistics at Penn State and Director of the Center for Multivariate Analysis. He has long been recognized as one of the world's top statisticians, and has been awarded 34 honorary doctorates from universities in 19 countries spanning 6 continents. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.

In 2011 he was recipient of the Royal Statistical Society's Guy Medal in Gold which is awarded triennially to those "who are judged to have merited a signal mark of distinction by reason of their innovative contributions to the theory or application of statistics". It can be awarded both to fellows (members) of the Society and to non-fellows. Since its inception 120 years ago the Gold Medal has been awarded to 34 distinguished statisticians. The first medal was awarded to Charles Booth in 1892. Only two statisticians, H. Cramer (Norwegian) and J. Neyman (Polish), outside Great Britain were awarded the Gold medal and C. R. Rao is the first non-European and non-American to receive the award.

Other awards he has received are the Gold Medal of Calcutta University, Wilks Medal of the American Statistical Association, Wilks Army Medal, Guy Medal in Silver of the Royal Statistical Society (UK), Megnadh Saha Medal and Srinivasa Ramanujan Medal of the Indian National Science Academy, J.C.Bose Gold Medal of Bose Institute and Mahalanobis Centenary Gold Medal of the Indian Science Congress, the Bhatnagar award of the Council of Scientific and Industrial Research, India and the Government of India honored him with the second highest civilian award, Padma Vibhushan, for “outstanding contributions to Science and Engineering / Statistics”, and also instituted a cash award in honor of C R Rao, “to be given once in two years to a young statistician for work done during the preceding 3 years in any field of statistics”.

For his outstanding achievements Rao has been honored with the establishment of an institute named after him, C.R.Rao Advanced Institute for Mathematics, Statistics and Computer Science, in the campus of the University of Hyderabad, India.

Table of Contents

1. Bootstrap methods for time series
2. Testing time series linearity: traditional and bootstrap methods
3. The quest for nonlinearity in Time Series
4. Modelling nonlinear and nonstationary time series,
5. Markov switching time series models
6. A review of robust estimation under conditional heteroscedasticity
7. Functional time series
8. Covariance matrix estimation in Time Series
9. Time series quantile regressions
10. Frequency domain techniques in the analysis of DNA sequences
11. Spatial time series modelling for fMRI data analysis in neurosciences
12. Count time series models
13. Locally stationary processes
14. Analysis of multivariate non-stationary time series using the localised Fourier Library
15. An alternative perspective on stochastic coefficient regression models
16. Hierarachical Bayesian models for space-time air pollution data
17. Karhunen-Loeve expansion for temporal and spatio-temporal processes
18. Statistical analysis of spatio-temporal models and their applications
19. Lévy-driven time series models for financial data
20. Discrete and continuous time extremes of stationary processesn
21. The estimation of Frequency
22. A wavelet variance primer
23. Time Series Analysis with R

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