{"title":"The asymptotic distribution of the fixed effects estimator for nonlinear regression","authors":"Sanghamitra Das","doi":"10.1016/0378-3758(93)90015-X","DOIUrl":null,"url":null,"abstract":"<div><p>For panel data sets, the fixed effects estimator available for the linear regression model is generalized to the nonlinear regression case. The asymptotic distribution of the estimator and its computational procedure is given. The estimator is then illustrated by estimating fuel coefficient of cement using panel data on cement plants in the U.S.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"35 3","pages":"Pages 269-277"},"PeriodicalIF":0.8000,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0378-3758(93)90015-X","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Planning and Inference","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/037837589390015X","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 1
Abstract
For panel data sets, the fixed effects estimator available for the linear regression model is generalized to the nonlinear regression case. The asymptotic distribution of the estimator and its computational procedure is given. The estimator is then illustrated by estimating fuel coefficient of cement using panel data on cement plants in the U.S.
期刊介绍：
The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.
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