使用随机缩放对具有高斯创新的超临界 AR(2) 过程的 LSE 进行混合收敛

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Metrika Pub Date : 2023-12-27 DOI:10.1007/s00184-023-00936-y

Mátyás Barczy, Fanni Nedényi, Gyula Pap

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引用次数: 0

摘要

我们证明了具有不同绝对值实特征根的高斯创新的 2 阶超临界自回归过程的自回归参数最小二乘估计值的混合收敛性。我们使用适当的随机缩放，使得极限分布是集中在由绝对值较大的特征根所决定的一维射线上的二维正态分布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Mixing convergence of LSE for supercritical AR(2) processes with Gaussian innovations using random scaling

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Mixing convergence of LSE for supercritical AR(2) processes with Gaussian innovations using random scaling

We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with Gaussian innovations having real characteristic roots with different absolute values. We use an appropriate random scaling such that the limit distribution is a two-dimensional normal distribution concentrated on a one-dimensional ray determined by the characteristic root having the larger absolute value.

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来源期刊

Metrika 数学-统计学与概率论

CiteScore

1.50

自引率

14.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.