Download PDF by Wai Keung Li, David A. Stanford, Hao Yu: Advances in Time Series Methods and Applications : The A.

By Wai Keung Li, David A. Stanford, Hao Yu

ISBN-10: 1493965670

ISBN-13: 9781493965670

ISBN-10: 1493965689

ISBN-13: 9781493965687

This quantity stories and summarizes a few of A. I. McLeod's major contributions to time sequence research. It additionally comprises unique contributions to the sphere and to similar parts through contributors of the festschrift held in June 2014 and associates of Dr. McLeod. protecting a various variety of state of the art themes, this quantity good balances utilized and theoretical examine throughout fourteen contributions via specialists within the box. will probably be of curiosity to researchers and practitioners in time sequence, econometricians, and graduate scholars in time sequence or econometrics, in addition to environmental statisticians, information scientists, statisticians drawn to graphical versions, and researchers in quantitative danger management.

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Garay-Malpartida, H. , Sogayar, M. , et al. (2007). Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Systems Biology, 1, 39. 13. Geyer, C. (1994). On the asymptotics of constrained M-estimation. The Annals of Statistics, 22, 1993–2010. 14. Hannan, E. J. (1970). Multiple time series. New York: Wiley. 15. Hannan, E. , & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, B41, 190–195. 16.

K K ,1 , φ11,2 , . . , φ K K ,2 , . . , phi i j,k , . . , φ11,h , . . , φ K K ,h . (10) Note that the index l in (9) corresponds to the l-th element of the vector φ, and the index (i j, k) in (10) corresponds to the (i, j)-th element of the matrix Φ k . The relation between (i, j, k) and l is bijective and defined by l = f (i, j, k) = (k − 1)K 2 + ( j − 1)K + i (11) where l = 1, 2, . . , (h K 2 ), i, j = 1, 2, . . , K , and k = 1, 2, . . , h. The true parameter matrix is Φ o = (Φ o1 , .

Table 2 shows the distribution of the aLASSO estimates for the VAR order. e. VAR(10) models) most frequently and most of time (83 %). To overcome the issue, we may employ a two-step procedure: First, use the OLS procedure plus the BIC criteria or the PLAC to identify the VAR order; second, apply the aLASSO to get a sparse model. This two-step procedure would work very well. Alternatively, we propose the doubly adaptive LASSO (daLASSO), or partial lag autocorrelation or PLAC-weighted adaptive LASSO.

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Advances in Time Series Methods and Applications : The A. Ian McLeod Festschrift by Wai Keung Li, David A. Stanford, Hao Yu

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