{"title":"带乘法误差的空间回归及其在激光雷达测量中的应用","authors":"Hojun You, Wei-Ying Wu, Chae Young Lim, Kyubaek Yoon, Jongeun Choi","doi":"10.1007/s42952-024-00282-3","DOIUrl":null,"url":null,"abstract":"<p>Multiplicative errors in addition to spatially referenced observations often arise in geodetic applications, particularly with light detection and ranging (LiDAR) measurements. However, regression involving multiplicative errors remains relatively unexplored in such applications. In this regard, we present a penalized modified least squares estimator to handle the complexities of a multiplicative error structure while identifying significant variables in spatially dependent observations. The proposed estimator can be also applied to classical additive error spatial regression. By establishing asymptotic properties of the proposed estimator under increasing domain asymptotics with stochastic sampling design, we provide a rigorous foundation for its effectiveness. A comprehensive simulation study confirms the superior performance of our proposed estimator in accurately estimating and selecting parameters, outperforming existing approaches. To demonstrate its real-world applicability, we employ our proposed method, along with other alternative techniques, to estimate a rotational landslide surface using LiDAR measurements. The results highlight the efficacy and potential of our approach in tackling complex spatial regression problems involving multiplicative errors.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"264 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial regression with multiplicative errors, and its application with LiDAR measurements\",\"authors\":\"Hojun You, Wei-Ying Wu, Chae Young Lim, Kyubaek Yoon, Jongeun Choi\",\"doi\":\"10.1007/s42952-024-00282-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Multiplicative errors in addition to spatially referenced observations often arise in geodetic applications, particularly with light detection and ranging (LiDAR) measurements. However, regression involving multiplicative errors remains relatively unexplored in such applications. In this regard, we present a penalized modified least squares estimator to handle the complexities of a multiplicative error structure while identifying significant variables in spatially dependent observations. The proposed estimator can be also applied to classical additive error spatial regression. By establishing asymptotic properties of the proposed estimator under increasing domain asymptotics with stochastic sampling design, we provide a rigorous foundation for its effectiveness. A comprehensive simulation study confirms the superior performance of our proposed estimator in accurately estimating and selecting parameters, outperforming existing approaches. To demonstrate its real-world applicability, we employ our proposed method, along with other alternative techniques, to estimate a rotational landslide surface using LiDAR measurements. The results highlight the efficacy and potential of our approach in tackling complex spatial regression problems involving multiplicative errors.</p>\",\"PeriodicalId\":49992,\"journal\":{\"name\":\"Journal of the Korean Statistical Society\",\"volume\":\"264 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Korean Statistical Society\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s42952-024-00282-3\",\"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":"Journal of the Korean Statistical Society","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s42952-024-00282-3","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Spatial regression with multiplicative errors, and its application with LiDAR measurements
Multiplicative errors in addition to spatially referenced observations often arise in geodetic applications, particularly with light detection and ranging (LiDAR) measurements. However, regression involving multiplicative errors remains relatively unexplored in such applications. In this regard, we present a penalized modified least squares estimator to handle the complexities of a multiplicative error structure while identifying significant variables in spatially dependent observations. The proposed estimator can be also applied to classical additive error spatial regression. By establishing asymptotic properties of the proposed estimator under increasing domain asymptotics with stochastic sampling design, we provide a rigorous foundation for its effectiveness. A comprehensive simulation study confirms the superior performance of our proposed estimator in accurately estimating and selecting parameters, outperforming existing approaches. To demonstrate its real-world applicability, we employ our proposed method, along with other alternative techniques, to estimate a rotational landslide surface using LiDAR measurements. The results highlight the efficacy and potential of our approach in tackling complex spatial regression problems involving multiplicative errors.
期刊介绍:
The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.