Estimation of nonlinear models with Berkson measurement errors
By Liqun Wang. 2004
82 citations recorded by the source corpus.
This paper is concerned with general nonlinear regression models where the predictor variables are subject to Berkson-type measurement errors. The measurement errors are assumed to have a general parametric distribution, which is not necessarily normal. In addition, the distribution of the random error in the regression equation is nonparametric. A minimum distance estimator is proposed, which is based on the first two conditional moments of the response variable given the observed predictor variables. To overcome the possible computational difficulty of minimizing an objective function which involves multiple integrals, a simulation-based estimator is constructed. Consistency and asymptotic normality for both estimators are derived under fairly general regularity conditions.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f222fcb226db0e036735087727fcd7bbee91d1f3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
In Statistics and Probability
Citations in the corpus
- Second-order nonlinear least squares estimation (2008, methodology): “The proposed estimation method was first used by Wang (2003, 2004) to deal with the measurement error problems in nonlinear regression models.”