AR PROCESSES LASSO PROCEDURE SERIES

M., Lage-Castellanos, A., Vega-Hernández, M., Bosch-Bayard, J., Melie-Garía, L., et al.

AR PROCESSES LASSO PROCEDURE SERIES

Journal of the Royal Statistical Society, Series B, 58(1), 267–288. Regression shrinkage and selection via the lasso. Statistics and Probability Letters, 80, 1705–1712. Subset selection for vector autoregressive processes via the adaptive LASSO. Lag weighted lasso for time series model. Journal of Multivariate Analysis, 102(3), 528–549. Autoregressive process modeling via the LASSO procedure. Estimating high-dimensional time series models. New introduction to multiple time series analysis. University of Western Ontario - Electronic Thesis and Dissertation Repository. The doubly adaptive LASSO methods for time series analysis. CREATES research paper 2012-12, Aarhus University. Oracle inequalities for high dimensional vector autoregressions. CREATES research papers 2012-05, Aarhus University. On the oracle property of the adaptive lasso in stationary and nonstationary autoregressions. Computational Statistics and Data Analysis, 52, 3645–3657. Subset selection for vector autoregressive processes using LASSO. Partial lag autocorrelation and partial process autocorrelation for vector time series, with applications. In JMLR: Workshop and conference proceedings (Vol. Sparse causal discovery in multivatiate time series. Journal of the Royal Statistical Society, B41, 190–195. The determination of the order of an autoregression. On the asymptotics of constrained M-estimation. Modeling gene expression regulatory networks with the sparse vector autoregressive model. M., Yamaguchi, R., Miyano, S., Sogayar, M. Journal of the American Statistical Association, 96, 1348–1360.įujita, A., Sato, J. Variable selection via nonconcave penalized likelihood and its oracle properties. Review of the Institute of International Statistics, 28, 233–244.Įfron, B., Johnstone, I., & Tibshirani, R.

IEEE Transactions on Iinformation Theory, 52(1), 6–18.ĭurbin, J.

Stable recovery of sparse overcomplete representations in the presence of noise. Statistics and Its Interface, 4, 197–205.ĭonoho, D. Subset ARMA selection via the adaptive Lasso. thesis, University of Nottingham.Ĭhen, K., & Chan, K. Goodness of fit and lasso variable selection in time series analysis.

Journal of Statistical Planning and Inference, 143, 691–715.Ĭhand, S. An alternative to unit root tests: bridge estimators differentiate between nonstationary versus stationary models and select optimal lag.

Proceedings of the American Mathematical Society, 12, 788–792.Ĭaner, M., & Knight, K. The Lindeberg-Levy theorem for martingales. Annals of the Institute of Statistical Mathematics, 30(Part A), 9–14.īillingsley, P. A Bayesian analysis of the minimum AIC procedure. IEEE Transactions on Automatic Control, AC–19, 716–723.Īkaike, H. A new look at the statistical model identification. Annals of the Institute of Statistical Mathematics, 21, 243–247.Īkaike, H. Fitting autoregressive models for prediction. Mathematics Subject Classifications (2010)Īkaike, H.

The procedure excels in terms of VAR lag order identification. The procedure shows promising results for VAR models. The existing papers ignore the partial lag autocorrelation information inherent in a VAR process. The procedure is doubly adaptive in the sense that its adaptive weights are formulated as functions of the norms of the partial lag autocorrelation matrix function (Heyse, 1985, ) and Yule–Walker or ordinary least squares estimates of a vector time series. We propose the doubly adaptive LASSO (daLASSO), or PLAC-weighted adaptive LASSO, for modelling stationary vector autoregressive processes. The LASSO (Tibshirani, J R Stat Soc Ser B 58(1):267–288, 1996, ) and the adaptive LASSO (Zou, J Am Stat Assoc 101:1418–1429, 2006, ) are popular in regression analysis for their advantage of simultaneous variable selection and parameter estimation, and also have been applied to autoregressive time series models.

Ar processes lasso procedure