By Wai Keung Li, David A. Stanford, Hao Yu
This quantity experiences and summarizes a few of A. I. McLeod's major contributions to time sequence research. It additionally includes unique contributions to the sphere and to similar components by means of individuals of the festschrift held in June 2014 and acquaintances of Dr. McLeod. protecting a various diversity of cutting-edge issues, this quantity good balances utilized and theoretical examine throughout fourteen contributions through specialists within the box. it will likely be of curiosity to researchers and practitioners in time sequence, econometricians, and graduate scholars in time sequence or econometrics, in addition to environmental statisticians, info scientists, statisticians drawn to graphical types, and researchers in quantitative threat management.
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This quantity studies and summarizes a few of A. I. McLeod's major contributions to time sequence research. It additionally includes unique contributions to the sector and to comparable parts through contributors of the festschrift held in June 2014 and acquaintances of Dr. McLeod. masking a various diversity of cutting-edge themes, this quantity good balances utilized and theoretical examine throughout fourteen contributions by means of specialists within the box.
Extra resources for Advances in Time Series Methods and Applications : The A. Ian McLeod Festschrift
Heyse, J. (1985). Partial lag autocorrelation and partial process autocorrelation for vector time series, with applications. D. dissertation, Temple University. 18. , & Chang, Y. (2008). Subset selection for vector autoregressive processes using LASSO. Computational Statistics and Data Analysis, 52, 3645–3657. 19. , & Fu, W. (2000). Asymptotics for LASSO-type estimators. The Annals of Statistics, 28, 1356–1378. 20. Kock, A. B. (2012). On the oracle property of the adaptive lasso in stationary and nonstationary autoregressions.
21. Kock, A. , & Callot, L. A. F. (2012). Oracle inequalities for high dimensional vector autoregressions. CREATES research paper 2012-12, Aarhus University. 22. Liu, Z. Z. (2014). The doubly adaptive LASSO methods for time series analysis. University of Western Ontario - Electronic Thesis and Dissertation Repository. Paper 2321. 23. Lütkepohl, H. (2006). New introduction to multiple time series analysis. Berlin: Springer. 24. Medeiros, M. C, & Mendes, E. F. (2012). Estimating high-dimensional time series models.
Theorem 2 assures that using the daLASSO Student is able to statistically identify the sparsity √ structure as if he knew S and estimate the coefficients with T -consistency. Theorem 2 is therefore useful for Student, the data analyst, to assess the VAR models fitted via the daLASSO. Remarks: da L da L da L 1. Although the asymptotic distributions of φˆ T,S and φˆ Sˆ T are identical, φˆ T,S and da L da L φˆ Sˆ T represent different identities; φˆ T,S is the daLASSO estimator for the true da L non-zero parameter vector unknown in advance whereas φˆ Sˆ T is the vector for non-zeros estimated by the daLASSO.
Advances in Time Series Methods and Applications : The A. Ian McLeod Festschrift by Wai Keung Li, David A. Stanford, Hao Yu