大数据遍历扩散模型的自适应高效分析

IF 0.7 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes Pub Date : 2021-03-27 DOI:10.1007/s11203-021-09241-9

L. Galtchouk, S. Pergamenshchikov

引用次数: 4

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Adaptive efficient analysis for big data ergodic diffusion models

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来源期刊

Statistical Inference for Stochastic Processes STATISTICS & PROBABILITY-

CiteScore

1.30

自引率

12.50%

发文量

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