Nonparametric estimation for random effects models driven by fractional Brownian motion using Hermite polynomials

IF 0.7 Q3 STATISTICS & PROBABILITY Statistical Inference for Stochastic Processes Pub Date : 2023-12-02 DOI:10.1007/s11203-023-09302-1
Hamid El Maroufy, Souad Ichi, Mohamed El Omari, Yousri Slaoui
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利用赫米特多项式对分数布朗运动驱动的随机效应模型进行非参数估计
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来源期刊
CiteScore
1.30
自引率
12.50%
发文量
19
期刊介绍: Statistical Inference for Stochastic Processes aims to publish high quality papers devoted to inference in either discrete or continuous time stochastic processes. This includes topics such as ARMA processes, GARCH processes and other time series models, as well as diffusion type processes, point processes, random fields and Markov processes. Papers related to spatial models and empirical processes are also within the scope of the journal. Special focus is placed on methodological advances and sound theoretical results, but submissions that expose potential applications of the developed theory to finance, insurance, economics, biology, physics and engineering are very much encouraged. Officially cited as: Stat Inference Stoch Process
期刊最新文献
Projection-based white noise and goodness-of-fit tests for functional time series A model specification test for nonlinear stochastic diffusions with delay The distribution of the maximum likelihood estimates of the change point and their relation to random walks Weak convergence of the conditional U-statistics for locally stationary functional time series Nonparametric estimation for random effects models driven by fractional Brownian motion using Hermite polynomials
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