Low order approximations in deconvolution and regression with errors in variables
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References listed on IDEAS
- John Staudenmayer & David Ruppert, 2004. "Local polynomial regression and simulation-extrapolation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 17-30.
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- Eric Weese & Masayoshi Hayashi & Masashi Nishikawa, 2015.
"Inefficiency and Self-Determination: Simulation-based Evidence from Meiji Japan,"
Discussion Paper Series
DP2015-35, Research Institute for Economics & Business Administration, Kobe University.
- Eric Weese & Masayoshi Hayashi & Masashi Nishikawa, 2016. "Inefficiency and Self-Determination: Simulation-based evidence from Meiji Japan," Discussion Papers 1627, Graduate School of Economics, Kobe University.
- Eric Weese & Masayoshi Hayashi & Masashi Nishikawa, 2015. "Inefficiency and Self-Determination: Simulation-Based Evidence From Meiji Japan," Working Papers 1050, Economic Growth Center, Yale University.
- Eric Weese & Masayoshi Hayashi & Masashi Nishikawa, 2015. "Inefficiency and Self-Determination: Simulation-based Evidence from Meiji Japan," CIRJE F-Series CIRJE-F-989, CIRJE, Faculty of Economics, University of Tokyo.
- William Horrace & Christopher Parmeter, 2011.
"Semiparametric deconvolution with unknown error variance,"
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Springer, vol. 35(2), pages 129-141, April.
- William C. Horrace & Christopher F. Parmeter, 2008. "Semiparametric Deconvolution with Unknown Error Variance," Center for Policy Research Working Papers 104, Center for Policy Research, Maxwell School, Syracuse University.
- Carrasco, Marine & Florens, Jean-Pierre, 2011. "A Spectral Method For Deconvolving A Density," Econometric Theory, Cambridge University Press, vol. 27(03), pages 546-581, June.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355, June.
- Thomas, Laine & Stefanski, Leonard A. & Davidian, Marie, 2013. "Moment adjusted imputation for multivariate measurement error data with applications to logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 15-24.
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