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73![Homework 4 solutions Joe Neeman October 27, We began by looking at the ACF of the original data sequence (Figure 1), which seems to decay very slowly. In particular, the process is probably not an ARMA process. T Homework 4 solutions Joe Neeman October 27, We began by looking at the ACF of the original data sequence (Figure 1), which seems to decay very slowly. In particular, the process is probably not an ARMA process. T](https://www.pdfsearch.io/img/0fca59c396e40d68c5ba93998d35f963.jpg) | Add to Reading ListSource URL: www.stat.berkeley.eduLanguage: English - Date: 2010-11-23 19:26:03
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74![Introduction to Time Series Analysis. Lecture 6. Peter Bartlett www.stat.berkeley.edu/∼bartlett/courses/153-fall2010 Last lecture: 1. Causality Introduction to Time Series Analysis. Lecture 6. Peter Bartlett www.stat.berkeley.edu/∼bartlett/courses/153-fall2010 Last lecture: 1. Causality](https://www.pdfsearch.io/img/2e9da07957e7ed7b33a78318273c2c97.jpg) | Add to Reading ListSource URL: www.stat.berkeley.eduLanguage: English - Date: 2010-09-14 17:35:35
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75![Estimation in High-dimensional Vector Autoregressive Models with Noisy Data Kam Chung Wong1 and Ambuj Tewari2 1 Department of Statistics, University of Michigan, Ann Arbor Estimation in High-dimensional Vector Autoregressive Models with Noisy Data Kam Chung Wong1 and Ambuj Tewari2 1 Department of Statistics, University of Michigan, Ann Arbor](https://www.pdfsearch.io/img/aee3cee3677c8d19085ec7eb15b9a1f1.jpg) | Add to Reading ListSource URL: ctools.umich.eduLanguage: English |
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76![Introduction to Time Series Analysis. Lecture 1. Peter Bartlett 1. Organizational issues. 2. Objectives of time series analysis. Examples. 3. Overview of the course. 4. Time series models. Introduction to Time Series Analysis. Lecture 1. Peter Bartlett 1. Organizational issues. 2. Objectives of time series analysis. Examples. 3. Overview of the course. 4. Time series models.](https://www.pdfsearch.io/img/b0c2ab262ac47e91fc8a23227fe5c54e.jpg) | Add to Reading ListSource URL: www.stat.berkeley.eduLanguage: English - Date: 2010-08-26 19:53:36
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77![Models for non-stationary series - a very brief, albeit useful, intro A time series yt is stationary if, roughly, its features are time invariant. In particular, E(yt ) = µ not a function of time! Var(yt ) = σ 2 not a Models for non-stationary series - a very brief, albeit useful, intro A time series yt is stationary if, roughly, its features are time invariant. In particular, E(yt ) = µ not a function of time! Var(yt ) = σ 2 not a](https://www.pdfsearch.io/img/5c30b51d04c9c3e343c0c6a4117a682f.jpg) | Add to Reading ListSource URL: www.nyu.eduLanguage: English - Date: 2012-08-21 10:10:54
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78![Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings Nikou Günnemann Stephan Günnemann Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings Nikou Günnemann Stephan Günnemann](https://www.pdfsearch.io/img/ef344806da392e61c9703f67eea643cb.jpg) | Add to Reading ListSource URL: www.cs.cmu.eduLanguage: English - Date: 2014-01-26 13:10:01
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79![doi:pan/mpl001 Random Coefficient Models for Time-Series–Cross-Section Data: Monte Carlo Experiments Nathaniel Beck doi:pan/mpl001 Random Coefficient Models for Time-Series–Cross-Section Data: Monte Carlo Experiments Nathaniel Beck](https://www.pdfsearch.io/img/084974c3b36b65cbb8b95f9412a0bb31.jpg) | Add to Reading ListSource URL: www.nyu.eduLanguage: English - Date: 2012-08-21 10:10:55
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80![DIVISION OF THE HUMANITIES AND SOCIAL SCIENCES CALIFORNIA INSTITUTE OF TECHNOLOGY PASADENA, CALIFORNIARANDOM COEFFICIENT MODELS FOR TIME-SERIES–CROSS-SECTION DIVISION OF THE HUMANITIES AND SOCIAL SCIENCES CALIFORNIA INSTITUTE OF TECHNOLOGY PASADENA, CALIFORNIARANDOM COEFFICIENT MODELS FOR TIME-SERIES–CROSS-SECTION](https://www.pdfsearch.io/img/d39a3ec1b2d4a73a7277cdb3ab2732e8.jpg) | Add to Reading ListSource URL: www.nyu.eduLanguage: English - Date: 2012-08-21 10:10:53
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