Nonlinear regression models for heterogeneous data with massive outliers
- Authors
- Jung, Yoonsuh
- Issue Date
- 11-6월-2019
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- Case-specific parameters; generalized additive models; heteroscedasticity; nonlinear regression; outliers; robust regression
- Citation
- JOURNAL OF APPLIED STATISTICS, v.46, no.8, pp.1456 - 1477
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF APPLIED STATISTICS
- Volume
- 46
- Number
- 8
- Start Page
- 1456
- End Page
- 1477
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/64781
- DOI
- 10.1080/02664763.2018.1552666
- ISSN
- 0266-4763
- Abstract
- The income or expenditure-related data sets are often nonlinear, heteroscedastic, skewed even after the transformation, and contain numerous outliers. We propose a class of robust nonlinear models that treat outlying observations effectively without removing them. For this purpose, case-specific parameters and a related penalty are employed to detect and modify the outliers systematically. We show how the existing nonlinear models such as smoothing splines and generalized additive models can be robustified by the case-specific parameters. Next, we extend the proposed methods to the heterogeneous models by incorporating unequal weights. The details of estimating the weights are provided. Two real data sets and simulated data sets show the potential of the proposed methods when the nature of the data is nonlinear with outlying observations.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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