Time series analysis and its applications

@inproceedings{Shumway2000TimeSA,
  title={Time series analysis and its applications},
  author={Robert Shumway and David S. Stoffer},
  year={2000},
  url={https://api.semanticscholar.org/CorpusID:117869442}
}
Characteristics of Time Series * Time Series Regression and ARIMA Models * Dynamic Linear Models and Kalman Filtering * Spectral Analysis and Its Applications.
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1,191 Citations
Highly Influential Citations
83
Background Citations
221
Methods Citations
255
Results Citations
6

1,191 Citations

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This chapter examines the linear mixed models from Chap. 5 that have traditionally been used to analyze time series data. It also examines spectral distributions/densities and linear filtering of…

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Dynamic Mixed Models for Irregularly Observed Time Series

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Robust Estimation for Multivariate Time Series

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Robust time series estimation

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Estimation of a nonparametric regression spectrum for multivariate time series

Empirical Spectral Processes and Nonparametric Maximum Likelihood Estimation for Time Series

We survey recent developments in the theory of empirical spectral processes indexed by functions and their applications to nonparametric maximum Whittle likelihood estimation for times series. The…

Time Domain Analysis

This chapter develops Box-Jenkins models. These involve applying the linear filters of Chap. 6 to white noise. It also introduces state-space models and the Kalman filter.

Order Patterns in Time Series

Two order functions are introduced which characterize a time series in a way similar to autocorrelation, and all finite‐dimensional distributions are obtained from the one‐dimensional distribution plus the order structure of a typical time series.
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