Search Results - subject:"Covariance matrix"

Mittag-Leffler vector random fields with Mittag-Leffler direct and cross covariance functions

Ma, Chunsheng - In: Annals of the Institute of Statistical Mathematics 65 (2013) 5, pp. 941-958

-order moments, a Mittag-Leffler vector random field is characterized by its mean function and its covariance matrix function, just …

Persistent link: https://www.econbiz.de/10010848634

Long memory conditional volatility and asset allocation

Harris, Richard D.F.; Nguyen, Anh - In: International Journal of Forecasting 29 (2013) 2, pp. 258-273

covariance matrix of returns over both short and long horizons, using the asset allocation framework of Engle and Colacito (2006 … memory models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and …

Persistent link: https://www.econbiz.de/10011051470

Error covariance matrix estimation using ridge estimator

Luo, June; Kulasekera, K.B. - In: Statistics & Probability Letters 83 (2013) 1, pp. 257-264

This article considers sparse covariance matrix estimation of high dimension. In contrast to the existing methods which … adaptive thresholding technique to estimate the error covariance matrix in high dimensional factor model. By obtaining the … true covariance matrix. Our thresholding estimator can be applied to more scenarios and is shown to have comparable rate of …

Persistent link: https://www.econbiz.de/10011039832

K-distributed vector random fields in space and time

Ma, Chunsheng - In: Statistics & Probability Letters 83 (2013) 4, pp. 1143-1150

real line. We derive the mean and covariance matrix functions of these K-distributed vector random fields, as well as the …

Persistent link: https://www.econbiz.de/10011040072

On the Monge–Ampère equation for characterizing gamma-Gaussian model

Kokonendji, Célestin C.; Masmoudi, Afif - In: Statistics & Probability Letters 83 (2013) 7, pp. 1692-1698

determinant of its covariance matrix, named the generalized variance function. Then, we show that its modified Lévy measure is of …

Persistent link: https://www.econbiz.de/10011040154

A note on the variance of the square components of a normal multivariate within a Euclidean ball

Palombi, Filippo; Toti, Simona - In: Journal of Multivariate Analysis 122 (2013) C, pp. 355-376

vector in v≥1 dimensions, with zero mean and covariance matrix Λ=diag(λ), and Bv(ρ) is a centered v-dimensional Euclidean …] to perform a reconstruction of Λ from the covariance matrix of X conditioned to Bv(ρ). In the regime of strong truncation …

Persistent link: https://www.econbiz.de/10011041896

Minimax optimal estimation of general bandable covariance matrices

Xue, Lingzhou; Zou, Hui - In: Journal of Multivariate Analysis 116 (2013) C, pp. 45-51

Cai et al. (2010) [4] have studied the minimax optimal estimation of a collection of large bandable covariance matrices whose off-diagonal entries decay to zero at a polynomial rate. They have shown that the minimax optimal procedures are fundamentally different under Frobenius and spectral...

Persistent link: https://www.econbiz.de/10011041948

Linear models that allow perfect estimation

Christensen, Ronald; Lin, Yong - In: Statistical Papers 54 (2013) 3, pp. 695-708

estimates and tests by replacing V with a pseudo-covariance matrix T = V + XUX′ for some nonnegative definite U such that …

Persistent link: https://www.econbiz.de/10010998641

Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator

Su, Liangjun; Ullah, Aman; Wang, Yun - In: Empirical Economics 45 (2013) 2, pp. 1009-1024

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, <CitationRef CitationID="CR7">2009</CitationRef>) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator...</citationref>

Persistent link: https://www.econbiz.de/10010994454

Two kinds of variance/covariance estimates in linear mixed models

Li, Zaixing - In: Metrika 76 (2013) 3, pp. 303-324

For longitudinal data, the within-subject covariance matrix plays an important role in statistical inference and it is … covariance matrix D <Subscript>1</Subscript> and the error variance σ <Superscript>2</Superscript> in linear mixed models. One is …

Persistent link: https://www.econbiz.de/10010995129