High-Dimensional Multivariate Time Series With Additional Structure

References

• American Lung Association (2015), “State of the Air: 2015,” Tech. Rep., American Lung Association.
   Google Scholar
• Barbosa, S. M. (2012), mAr: Multivariate AutoRegressive analysis, R package version 1.1-2.
   Google Scholar
• Basu, S., and Michailidis, G. (2015), “Regularized Estimation in Sparse High-Dimensional Time Series Models,” The Annals of Statistics, 43, 1535–1567.
   Web of Science ®Google Scholar
• Bates, D., and Maechler, M. (2016), Matrix: Sparse and Dense Matrix Classes and Methods, R package version 1.2-4.
   Google Scholar
• Bien, J., and Tibshirani, R. (2012), spcov: Sparse Estimation of a Covariance Matrix, R package version 1.01.
   Google Scholar
• Bühlmann, P., and van de Geer, S. (2011), Statistics for High-Dimensional Data: Methods, Theory and Applications, New York: Springer.
   Google Scholar
• Chen, G., Wan, X., Yang, G., and Zou, X. (2015), “Traffic-Related Air Pollution and Lung Cancer: A Meta-Analysis,” Thoracic Cancer, 6, 307–318.
   PubMed Web of Science ®Google Scholar
• Cressie, N., and Wikle, C. K. (2011), Statistics for Spatio-Temporal Data, New York: Wiley.
   Google Scholar
• Davis, R. A., Mikosch, T., and Cribben, I. (2012), “Towards Estimating Extremal Serial Dependence via the Bootstrapped Extremogram,” Journal of Econometrics, 170, 142–152.
   Web of Science ®Google Scholar
• Davis, R. A., Zang, P., and Zheng, T. (2016), “Sparse Vector Autoregressive Modeling,” Journal of Computational and Graphical Statistics, 25, 1077–1096.
   Web of Science ®Google Scholar
• Eichler, M. (2012), “Graphical Modelling of Multivariate Time Series,” Probability Theory and Related Fields, 153, 233–268.
   Web of Science ®Google Scholar
• Ensor, K. B., Raun, L., and Persse, D. (2013), “A Case-Crossover Analysis of Out-of-Hospital Cardiac Arrest and Air Pollution,” Circulation, 1192–1199.
   PubMed Web of Science ®Google Scholar
• Friedman, J., Hastie, T., and Tibshirani, R. (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, 33, 1–22.
   PubMed Web of Science ®Google Scholar
• Goulet, V., Dutang, C., Maechler, M., Firth, D., Shapira, M., Stadelmann, M., and [email protected] (2015), expm: Matrix Exponential, R package version 0.999-0.
   Google Scholar
• Hastie, T., and Efron, B. (2013), lars: Least Angle Regression, Lasso and Forward Stagewise, R package version 1.2.
   Google Scholar
• Hoek, G., Krishnan, R., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., and Kaufman, J. (2013), “Long-Term Air Pollution Exposure and Cardio-Respiratory Mortality: A Review,” Environmental Health, 12, 43.
   PubMed Web of Science ®Google Scholar
• Künsch, H. R. (1989), “The Jackknife and the Bootstrap for General Stationary Observations,” The Annals of Statistics, 17, 1217–1241.
   Web of Science ®Google Scholar
• Loh, P. L., and Wainwright, M. J. (2012), “High-Dimensional Regression With Noisy and Missing Data: Provable Guarantees With Nonconvexity,” The Annals of Statistics, 40, 1637–1664.
   Web of Science ®Google Scholar
• Lütkepohl, H. (2007), New Introduction to Multiple Time Series Analysis, New York: Springer Science & Business Media.
   Google Scholar
• Meinshausen, N., and Bühlmann, P. (2006), “High-Dimensional Graphs and Variable Selection With the LASSO,” The Annals of Statistics, 34, 1436–1462.
   Web of Science ®Google Scholar
• ——— (2010), “Stability Selection,” Journal of the Royal Statistical Society, Series B, 72, 417–473.
   Google Scholar
• Nguyen, H., Katzfuss, M., Cressie, N., and Braverman, A. (2014), “Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets,” Technometrics, 56, 174–185.
   Web of Science ®Google Scholar
• Politis, D. N., and Romano, J. P. (1994), “The Stationary Bootstrap,” Journal of the American Statistical Association, 89, 1303–1313.
   Web of Science ®Google Scholar
• R Core Team (2016), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Rao, S. T., Zurbenko, I. G., Neagu, R., Porter, P. S., Ku, J. Y., and Henry, R. F. (1997), “Space and Time Scales for Ambient Ozone Data,” Bulletin of the American Meteorological Society, 78, 2153–2166.
   Web of Science ®Google Scholar
• Ravikumar, P., Wainwright, M. J., and Lafferty, J. (2010), “High-Dimensional Ising Model Selection Using ℓ1-Regularized Logistic Regression,” The Annals of Statistics, 38, 1287–1319.
   Web of Science ®Google Scholar
• Rmetrics Core Team, Wuertz, D., Setz, T., and Chalabi, Y. (2015), timeSeries: Rmetrics—Financial Time Series Objects, R package version 3022.101.2.
   Google Scholar
• Song, S., and Bickel, P. J. (2011), “Large Vector Auto Regressions,” arXiv:1106.3915.
   Google Scholar
• South, A. (2011), “Rworldmap: A New R Package for Mapping Global Data,” The R Journal, 3, 35–43.
   Google Scholar
• Stigler, M. (2010), tsDyn: Threshold Cointegration: Overview and Implementation in R, R package version 0.7-2.
   Google Scholar
• Thompson, S. K. (2012), Sampling (3rd ed.), New York: Wiley.
   Google Scholar
• Wilson, G. T., Reale, M., and Haywood, J. (2015), Models for Dependent Time Series, Boca Raton, FL: CRC Press.
   Google Scholar
• World Health Organization (2014), “7 Million Premature Deaths Annually Linked to Air Pollution,” Tech. Rep., World Health Organization.
   Google Scholar