Properties of spectral covariance for linear processes with infinite variance

被引:10
作者
Damarackas, Julius [1 ]
Paulauskas, Vygantas [1 ,2 ]
机构
[1] Vilnius State Univ, Fac Math & Informat, LT-03225 Vilnius, Lithuania
[2] Vilnius State Univ, Inst Math & Informat, LT-08663 Vilnius, Lithuania
关键词
stable random vectors; measures of dependence; random linear processes; stochastic integrals; STABLE DISTRIBUTIONS; INNOVATIONS;
D O I
10.1007/s10986-014-9242-z
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
We consider a measure of dependence for symmetric alpha-stable random vectors, which was introduced by the second author in 1976. We demonstrate that this measure of dependence, which we suggest to call the spectral covariance, can be extended to random vectors in the domain of normal attraction of general stable vectors. We investigate the asymptotic of the spectral covariance function for linear stable (Ornstein-Uhlenbeck, log-fractional, linear-fractional) processes with infinite variance and show that, in comparison with the results on the properties of codifference of these processes, obtained two decades ago, the results for the spectral variance are obtained under more general conditions and calculations are simpler.
引用
收藏
页码:252 / 276
页数:25
相关论文
共 35 条
[11]   Parameter estimation for infinite variance fractional arima [J].
Kokoszka, PS ;
Taqqu, MS .
ANNALS OF STATISTICS, 1996, 24 (05) :1880-1913
[12]   WILD BOOTSTRAP OF THE SAMPLE MEAN IN THE INFINITE VARIANCE CASE [J].
Cavaliere, Giuseppe ;
Georgiev, Iliyan ;
Taylor, A. M. Robert .
ECONOMETRIC REVIEWS, 2013, 32 (02) :204-219
[13]   Recursive estimation for regression with infinite variance fractional ARIMA noise [J].
Thavaneswaran, A ;
Peiris, S .
MATHEMATICAL AND COMPUTER MODELLING, 2001, 34 (9-11) :1133-1137
[14]   Smoothed estimates for models with random coefficients and infinite variance innovations [J].
Thavaneswaran, A ;
Peiris, S .
MATHEMATICAL AND COMPUTER MODELLING, 2004, 39 (4-5) :363-372
[15]   Maximum likelihood estimators in regression models with infinite variance innovations [J].
Paulaauskas, V ;
Rachev, ST .
STATISTICAL PAPERS, 2003, 44 (01) :47-65
[16]   Maximum likelihood estimators in regression models with infinite variance innovations [J].
Vygantas Paulaauskas ;
Svetlozar T. Rachev .
Statistical Papers, 2003, 44 :47-65
[17]   The k-factor GARMA Process with Infinite Variance Innovations [J].
Diongue, Abdou Ka ;
Ndongo, Mor .
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2016, 45 (02) :420-437
[18]   ASYMPTOTIC PROPERTIES OF SELF-NORMALIZED LINEAR PROCESSES WITH LONG MEMORY [J].
Peligrad, Magda ;
Sang, Hailin .
ECONOMETRIC THEORY, 2012, 28 (03) :548-569
[19]   Inference for some time series models with random coefficients and infinite variance innovations [J].
Thavaneswaran, A ;
Peiris, S .
MATHEMATICAL AND COMPUTER MODELLING, 2001, 33 (8-9) :843-849
[20]   White Noise Array Gain for Minimum Variance Distortionless Response Beamforming With Fractional Lower Order Covariance [J].
Song, Aimin .
IEEE ACCESS, 2018, 6 :71581-71591