Statistical inference for stationary linear models with tapered data

被引:2
作者
Ginovyan, Mamikon S. [1 ]
Sahakyan, Artur A. [2 ]
机构
[1] Boston Univ, Dept Math & Stat, Boston, MA 02215 USA
[2] Yerevan State Univ, Dept Math & Mech, Yerevan, Armenia
关键词
Tapered data; stationary processes; spectral density; parametric and nonparametric estimation; goodness-of-fit test; EFFICIENT NONPARAMETRIC-ESTIMATION; TOEPLITZ QUADRATIC FUNCTIONALS; MAXIMUM-LIKELIHOOD-ESTIMATION; MINIMUM CONTRAST ESTIMATION; LONG-RANGE DEPENDENCE; CENTRAL-LIMIT-THEOREM; PARAMETER-ESTIMATION; SPECTRAL DENSITY; TRACE APPROXIMATIONS; GAUSSIAN-PROCESSES;
D O I
10.1214/21-SS134
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In this paper, we survey some recent results on statistical inference (parametric and nonparametric statistical estimation, hypotheses testing) about the spectrum of stationary models with tapered data. We also discuss some questions concerning tapered Toeplitz matrices and operators, central limit theorems for tapered Toeplitz type quadratic functionals, and tapered Fejer-type kernels and singular integrals. These are the main tools for obtaining the corresponding results, and also are of interest in themselves. The processes considered will be discrete-time and continuous-time Gaussian, linear or Levy-driven linear processes with memory.
引用
收藏
页码:154 / 194
页数:41
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