The Annals of Statistics

Covariate-adjusted nonlinear regression

Xia Cui, Wensheng Guo, Lu Lin, and Lixing Zhu

Full-text: Open access

Abstract

In this paper, we propose a covariate-adjusted nonlinear regression model. In this model, both the response and predictors can only be observed after being distorted by some multiplicative factors. Because of nonlinearity, existing methods for the linear setting cannot be directly employed. To attack this problem, we propose estimating the distorting functions by nonparametrically regressing the predictors and response on the distorting covariate; then, nonlinear least squares estimators for the parameters are obtained using the estimated response and predictors. Root n-consistency and asymptotic normality are established. However, the limiting variance has a very complex structure with several unknown components, and confidence regions based on normal approximation are not efficient. Empirical likelihood-based confidence regions are proposed, and their accuracy is also verified due to its self-scale invariance. Furthermore, unlike the common results derived from the profile methods, even when plug-in estimates are used for the infinite-dimensional nuisance parameters (distorting functions), the limit of empirical likelihood ratio is still chi-squared distributed. This property eases the construction of the empirical likelihood-based confidence regions. A simulation study is carried out to assess the finite sample performance of the proposed estimators and confidence regions. We apply our method to study the relationship between glomerular filtration rate and serum creatinine.

Article information

Source
Ann. Statist., Volume 37, Number 4 (2009), 1839-1870.

Dates
First available in Project Euclid: 18 June 2009

Permanent link to this document
https://projecteuclid.org/euclid.aos/1245332834

Digital Object Identifier
doi:10.1214/08-AOS627

Mathematical Reviews number (MathSciNet)
MR2533473

Zentralblatt MATH identifier
1168.62035

Subjects
Primary: 62J05: Linear regression 62G08: Nonparametric regression 62G20: Asymptotic properties

Keywords
Asymptotic behavior confidence region covariate-adjusted regression empirical likelihood kernel estimation nonlinear least squares

Citation

Cui, Xia; Guo, Wensheng; Lin, Lu; Zhu, Lixing. Covariate-adjusted nonlinear regression. Ann. Statist. 37 (2009), no. 4, 1839--1870. doi:10.1214/08-AOS627. https://projecteuclid.org/euclid.aos/1245332834


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