Adaptive robust efficient methods for periodic signal processing observed with colours noises /E. A. Pchelintsev, S. M. Pergamenshchikov, M. Marcokova

Pchelintsev, Evgeny A.
Электронный ресурс
Другой Автор
Pergamenshchikov, Serguei M.
Marcokova, Mariana
Источник
Advances in electrical and electronic engineering 2019 Vol. 17, № 3. P. 270-274
Аннотация
In this paper, we consider the problem of robust adaptive efficient estimating a periodic signal observed in the transmission channel with the dependent noise defined by non-Gaussian Ornstein-Uhlenbeck processes with unknown correlation properties. Adaptive model selection procedures, based on the shrinkage weighted least squares estimates, are proposed. The comparison between shrinkage and least squares methods is studied and the advantages of the shrinkage methods are analyzed. Estimation properties for proposed statistical algorithms are studied on the basis of the robust mean square accuracy defined as the maximum mean square estimation error over all possible values of unknown noise parameters. Sharp oracle inequalities for the robust risks have been obtained. The robust efficiency of the model selection procedure has been established.
Всего оценка: 0
Нет записей для отображения.
 
 
 
02584nab a2200385 c 4500
001
 
 
vtls000674070
003
 
 
RU-ToGU
005
 
 
20200129172600.0
007
 
 
cr |
008
 
 
200123|2019    xr      s         a eng d
024
7
$a 10.15598/aeee.v17i3.3132 $2 doi
035
$a to000674070
039
9
$a 202001291726 $b cat34 $c 202001231522 $d VLOAD $y 202001231519 $z VLOAD
040
$a RU-ToGU $b rus $c RU-ToGU
100
1
$a Pchelintsev, Evgeny A.
245
1
0
$a Adaptive robust efficient methods for periodic signal processing observed with colours noises $c E. A. Pchelintsev, S. M. Pergamenshchikov, M. Marcokova
504
$a Библиогр.: 12 назв.
520
3
$a In this paper, we consider the problem of robust adaptive efficient estimating a periodic signal observed in the transmission channel with the dependent noise defined by non-Gaussian Ornstein-Uhlenbeck processes with unknown correlation properties. Adaptive model selection procedures, based on the shrinkage weighted least squares estimates, are proposed. The comparison between shrinkage and least squares methods is studied and the advantages of the shrinkage methods are analyzed. Estimation properties for proposed statistical algorithms are studied on the basis of the robust mean square accuracy defined as the maximum mean square estimation error over all possible values of unknown noise parameters. Sharp oracle inequalities for the robust risks have been obtained. The robust efficiency of the model selection procedure has been established.
653
$a асимптотическая эффективность.
653
$a выбор модели
653
$a непараметрическая регрессия
653
$a Орнштейна-Уленбека процесс
653
$a периодические сигналы
653
$a робастный квадратический риск
653
$a оракульное неравенство
653
$a взвешенные оценки наименьших квадратов
655
4
$a статьи в журналах
700
1
$a Pergamenshchikov, Serguei M.
700
1
$a Marcokova, Mariana
773
0
$t Advances in electrical and electronic engineering $d 2019 $g Vol. 17, № 3. P. 270-274 $x 1336-1376
852
4
$a RU-ToGU
856
4
$u http://vital.lib.tsu.ru/vital/access/manager/Repository/vtls:000674070
908
$a статья
999
$a VIRTUA               
999
$a VTLSSORT0070*0080*0240*0350*0400*1000*2450*5040*5200*6530*6531*6532*6533*6534*6535*6536*6537*6550*7000*7001*7730*8520*8560*9080*9992
Нет комментариев.
Предмет
статьи в журналах
Резюме
In this paper, we consider the problem of robust adaptive efficient estimating a periodic signal observed in the transmission channel with the dependent noise defined by non-Gaussian Ornstein-Uhlenbeck processes with unknown correlation properties. Adaptive model selection procedures, based on the shrinkage weighted least squares estimates, are proposed. The comparison between shrinkage and least squares methods is studied and the advantages of the shrinkage methods are analyzed. Estimation properties for proposed statistical algorithms are studied on the basis of the robust mean square accuracy defined as the maximum mean square estimation error over all possible values of unknown noise parameters. Sharp oracle inequalities for the robust risks have been obtained. The robust efficiency of the model selection procedure has been established.