{"title":"Parameter estimation in optional semimartingale regression models","authors":"A. Melnikov, A. Pak","doi":"10.1080/02331888.2023.2242549","DOIUrl":null,"url":null,"abstract":"The paper is devoted to the problem of parameter estimation in a multivariate optional semimartingale regression model. The family of optional semimartingales is a rich class of stochastic processes that contains càdlàg semimartingales. In general, such processes do not admit càdlàg modifications, i.e. right-continuous with finite left-limits. The weighted least squares estimator is derived, and its strong consistency is proved under general conditions on regressors. Furthermore, sequential least squares estimates are systematically studied. It is shown that such estimates have a nice statistical property called fixed accuracy. Sequential estimation procedure developed in the paper works without restrictions on dimensions of unknown parameter and of observation process. The paper contains several examples of multivariate regressions to demonstrate our results and proposed techniques.","PeriodicalId":54358,"journal":{"name":"Statistics","volume":"27 1","pages":"1165 - 1201"},"PeriodicalIF":1.2000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/02331888.2023.2242549","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
The paper is devoted to the problem of parameter estimation in a multivariate optional semimartingale regression model. The family of optional semimartingales is a rich class of stochastic processes that contains càdlàg semimartingales. In general, such processes do not admit càdlàg modifications, i.e. right-continuous with finite left-limits. The weighted least squares estimator is derived, and its strong consistency is proved under general conditions on regressors. Furthermore, sequential least squares estimates are systematically studied. It is shown that such estimates have a nice statistical property called fixed accuracy. Sequential estimation procedure developed in the paper works without restrictions on dimensions of unknown parameter and of observation process. The paper contains several examples of multivariate regressions to demonstrate our results and proposed techniques.
期刊介绍：
Statistics publishes papers developing and analysing new methods for any active field of statistics, motivated by real-life problems. Papers submitted for consideration should provide interesting and novel contributions to statistical theory and its applications with rigorous mathematical results and proofs. Moreover, numerical simulations and application to real data sets can improve the quality of papers, and should be included where appropriate. Statistics does not publish papers which represent mere application of existing procedures to case studies, and papers are required to contain methodological or theoretical innovation. Topics of interest include, for example, nonparametric statistics, time series, analysis of topological or functional data. Furthermore the journal also welcomes submissions in the field of theoretical econometrics and its links to mathematical statistics.
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