Statistical Analysis of Stochastic Processes in Time (Cambridge Series in Statistical and Probabilistic Mathematics)

Description

This introduction to ways of modelling a wide variety of phenomena that occur over time is accessible to anyone with a basic knowledge of statistical ideas. J.K. Lindsey concentrates on tractable models involving simple processes for which explicit probability models, hence likelihood functions, can be specified. (These models are the most useful in statistical applications modelling empirical data.) Examples are drawn from physical, biological and social sciences, to show how the book's underlying ideas can be applied, and data sets and R code are supplied for them. Author resource page: http://popgen.unimaas.nl/~jlindsey/books.html

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1. Ebooks list page : 1996
2. Wavelet Methods for Time Series Analysis (Cambridge Series in Statistical and Probabilistic Mathematics)
3. Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics)
4. Statistical Analysis of Stochastic Processes in Time
5. Semiparametric Regression (Cambridge Series in Statistical and Probabilistic Mathematics)
6. Markov Chains (Cambridge Series in Statistical and Probabilistic Mathematics)
7. Elements of Distribution Theory (Cambridge Series in Statistical and Probabilistic Mathematics)
8. Design of Comparative Experiments (Cambridge Series in Statistical and Probabilistic Mathematics)
9. The Coordinate-Free Approach to Linear Models (Cambridge Series in Statistical and Probabilistic Mathematics)
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12. The Statistical Analysis of Failure Time Data (Wiley Series in Probability and Statistics)
13. Reconstructing Macroeconomics: A Perspective from Statistical Physics and Combinatorial Stochastic Processes
14. Stochastic Networks: Theory and Applications (Royal Statistical Society Lecture Note Series, 4)
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