{"title":"函数上标量回归结构中模态函数的条件密度估计&随机缺失的局部线性方法","authors":"Wahiba Bouabsa","doi":"10.15611/eada.2023.1.02","DOIUrl":null,"url":null,"abstract":"Abstract The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"27 1","pages":"17 - 32"},"PeriodicalIF":1.1000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random\",\"authors\":\"Wahiba Bouabsa\",\"doi\":\"10.15611/eada.2023.1.02\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.\",\"PeriodicalId\":11499,\"journal\":{\"name\":\"Econometrics\",\"volume\":\"27 1\",\"pages\":\"17 - 32\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15611/eada.2023.1.02\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15611/eada.2023.1.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
摘要
摘要本文研究了给定函数变量的标量响应变量的密度和模态函数的非参数估计。该估计是将随机缺失(Missing At Random, MAR)方法与局部线性方法相结合得到的。最后,还提供了基于模拟数据的比较研究,以说明有限样本的性能以及局部线性方法与MAR的有用性,即使数据中存在很小比例的异常值。
The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random
Abstract The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.