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Time Series and Statistics

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Cover of 'Time Series and Statistics'

Table of Contents

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    Book Overview
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    Chapter 1 Statistical Inference in Time Series
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    Chapter 2 ARIMA Models
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    Chapter 3 Autoregressive and Moving-average Time-series Processes
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    Chapter 4 Bayesian Inference
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    Chapter 5 Continuous and Discrete Time Models
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    Chapter 6 Edgeworth as a Statistician
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    Chapter 7 Ergodic Theory
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    Chapter 8 Estimation
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    Chapter 9 Factor Analysis
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    Chapter 10 Ronald Aylmer Fisher
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    Chapter 11 Forecasting
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    Chapter 12 Heteroskedasticity
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    Chapter 13 Harold Hotelling
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    Chapter 14 Hypothesis Testing
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    Chapter 15 Least Squares
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    Chapter 16 Likelihood
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    Chapter 17 Martingales
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    Chapter 18 Maximum Likelihood
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    Chapter 19 Meaningfulness and Invariance
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    Chapter 20 Mean Value
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    Chapter 21 Measurement
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    Chapter 22 Monte Carlo Methods
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    Chapter 23 Multivariate Time Series Models
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    Chapter 24 Non-parametric Statistical Methods
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    Chapter 25 Outliers
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    Chapter 26 Prediction
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    Chapter 27 Principal Components
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    Chapter 28 Randomization
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    Chapter 29 Random Variables
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    Chapter 30 Regression and Correlation Analysis
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    Chapter 31 Residuals
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    Chapter 32 Semiparametric Estimation
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    Chapter 33 Sequential Analysis
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    Chapter 34 Eugen Slutsky
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    Chapter 35 Spectral Analysis
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    Chapter 36 Spline Functions
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    Chapter 37 Stationary Time Series
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    Chapter 38 Statistical Decision Theory
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    Chapter 39 Statistical Inference
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    Chapter 40 Time Series Analysis
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    Chapter 41 Transformation of Statistical Variables
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    Chapter 42 Abraham Wald
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    Chapter 43 Wiener Process
Attention for Chapter 28: Randomization
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Chapter title
Randomization
Chapter number 28
Book title
Time Series and Statistics
Published by
Palgrave Macmillan, London, January 1990
DOI 10.1007/978-1-349-20865-4_28
Book ISBNs
978-0-333-49551-3, 978-1-349-20865-4
Authors

James O. Berger