{"title":"Projection-based white noise and goodness-of-fit tests for functional time series","authors":"Mihyun Kim, Piotr Kokoszka, Gregory Rice","doi":"10.1007/s11203-024-09315-4","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":43294,"journal":{"name":"Statistical Inference for Stochastic Processes","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Inference for Stochastic Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11203-024-09315-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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
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