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Daisuke Kurisu and Taisuke Otsu
By utilizing intermediate Gaussian approximations, this paper establishes asymptotic linear representations of nonparametric deconvolution estimators for the classical measurement error model with repeated measurements. ...Read more...
19 July 2021
This paper studies the uniform convergence rates of Li and Vuong's (1998) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015) for the classical measurement error model, where repe...Read more...
22 July 2019
Hao Dong and Taisuke Otsu
In estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement errors are present in covariates. We propose an estimator for such ...Read more...
27 November 2018
Taisuke Otsu and Luke Taylor
This paper considers specification testing for regression models with errors-in-variables and proposes a test statistic comparing the distance between the parametric and nonparametric fits based on deconvolution techniqu...Read more...
15 August 2016
Karun Adusumilli and Taisuke Otsu
This paper considers nonparametric instrumental variable regression when the endogenous variable is contaminated with classical measurement error. Existing methods are inconsistent in the presence of measurement error. W...Read more...
21 July 2015
Oliver Linton and Yoon-Jae Whang
We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from di...Read more...