{"title":"基于指数平方损失的AR模型鲁棒变量选择","authors":"Yaxin Wu, Yunquan Song, Xijun Liang, Yujie Gai","doi":"10.1214/21-bjps524","DOIUrl":null,"url":null,"abstract":"Time series analysis is widely used in the fields of economics, ecology and medicine. Robust variable selection procedures through penalized regression have been gaining increased attention. In our work, a robust penalized regression estimator based on exponential squared loss for autoregressive (AR) models is proposed and discussed. The objective model with adaptive Lasso penalty realizes variable selection and parameter estimation simultaneously. Under some regular conditions, we establish the asymptotic and “Oracle” properties of the proposed estimator. In particular, the induced non-convex and non-differentiable mathematical programming problem offers challenges for solving algorithms. To solve this problem efficiently, we specially design a block coordinate descent (BCD) algorithm equipped with concave-convex process (CCCP) and provide a convergence guarantee. Numerical simulation studies are carried out to show that the proposed method is particularly robust and applicable compared with some recent methods when there are different types of noise or different intensity of noise. Furthermore, an application on a dataset of daily minimum temperature in Melbourne over 1981-1990 is performed.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exponential squared loss based robust variable selection of AR models\",\"authors\":\"Yaxin Wu, Yunquan Song, Xijun Liang, Yujie Gai\",\"doi\":\"10.1214/21-bjps524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Time series analysis is widely used in the fields of economics, ecology and medicine. Robust variable selection procedures through penalized regression have been gaining increased attention. In our work, a robust penalized regression estimator based on exponential squared loss for autoregressive (AR) models is proposed and discussed. The objective model with adaptive Lasso penalty realizes variable selection and parameter estimation simultaneously. Under some regular conditions, we establish the asymptotic and “Oracle” properties of the proposed estimator. In particular, the induced non-convex and non-differentiable mathematical programming problem offers challenges for solving algorithms. To solve this problem efficiently, we specially design a block coordinate descent (BCD) algorithm equipped with concave-convex process (CCCP) and provide a convergence guarantee. Numerical simulation studies are carried out to show that the proposed method is particularly robust and applicable compared with some recent methods when there are different types of noise or different intensity of noise. Furthermore, an application on a dataset of daily minimum temperature in Melbourne over 1981-1990 is performed.\",\"PeriodicalId\":51242,\"journal\":{\"name\":\"Brazilian Journal of Probability and Statistics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brazilian Journal of Probability and Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1214/21-bjps524\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brazilian Journal of Probability and Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/21-bjps524","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Exponential squared loss based robust variable selection of AR models
Time series analysis is widely used in the fields of economics, ecology and medicine. Robust variable selection procedures through penalized regression have been gaining increased attention. In our work, a robust penalized regression estimator based on exponential squared loss for autoregressive (AR) models is proposed and discussed. The objective model with adaptive Lasso penalty realizes variable selection and parameter estimation simultaneously. Under some regular conditions, we establish the asymptotic and “Oracle” properties of the proposed estimator. In particular, the induced non-convex and non-differentiable mathematical programming problem offers challenges for solving algorithms. To solve this problem efficiently, we specially design a block coordinate descent (BCD) algorithm equipped with concave-convex process (CCCP) and provide a convergence guarantee. Numerical simulation studies are carried out to show that the proposed method is particularly robust and applicable compared with some recent methods when there are different types of noise or different intensity of noise. Furthermore, an application on a dataset of daily minimum temperature in Melbourne over 1981-1990 is performed.
期刊介绍:
The Brazilian Journal of Probability and Statistics aims to publish high quality research papers in applied probability, applied statistics, computational statistics, mathematical statistics, probability theory and stochastic processes.
More specifically, the following types of contributions will be considered:
(i) Original articles dealing with methodological developments, comparison of competing techniques or their computational aspects.
(ii) Original articles developing theoretical results.
(iii) Articles that contain novel applications of existing methodologies to practical problems. For these papers the focus is in the importance and originality of the applied problem, as well as, applications of the best available methodologies to solve it.
(iv) Survey articles containing a thorough coverage of topics of broad interest to probability and statistics. The journal will occasionally publish book reviews, invited papers and essays on the teaching of statistics.