An Update on Measurement Error Modeling

Literature Cited

1. Amemiya Y. 1985.. Instrumental variable estimator for the nonlinear errors-in-variables model. . J. Econom. 28::273–90
   [Crossref] [Google Scholar]
2. Belloni A, Chernozhukov V, Kaul A. 2017a.. Confidence bands for coefficients in high dimensional linear models with error-in-variables. . arXiv:1703.00469 [math.ST]
3. Belloni A, Rosenbaum M, Tsybakov AB. 2017b.. Linear and conic programming estimators in high dimensional errors-in-variables models. . J. R. Stat. Soc. Ser. B 79::939–56
   [Crossref] [Google Scholar]
4. Berry SM, Carroll RJ, Ruppert D. 2002.. Bayesian smoothing and regression splines for measurement error problems. . J. Am. Stat. Assoc. 97::160–69
   [Crossref] [Google Scholar]
5. Bickel PJ, Klaassen CAJ, Ritov Y, Wellner JA. 1990.. Efficient and Adaptive Estimation for Semiparametric Models. New York:: Springer
6. Carroll RJ, Delaigle A, Hall P. 2007.. Non-parametric regression estimation from data contaminated by a mixture of Berkson and classical errors. . J. R. Stat. Soc. Ser. B 69::859–78
   [Crossref] [Google Scholar]
7. Carroll RJ, Hall P. 1988.. Optimal rates of convergence for deconvolving a density. . J. Am. Stat. Assoc. 83::1184–86
   [Crossref] [Google Scholar]
8. Carroll RJ, Spiegelman CH, Lan KKG, Bailey KT, Abbott RD. 1984.. On errors-in-variables for binary regression models. . Biometrika 71::19–25
   [Crossref] [Google Scholar]
9. Datta A, Zou H. 2017.. CoCoLasso for high-dimensional error-in-variables regression. . Ann. Stat. 45::2400–26
   [Crossref] [Google Scholar]
10. Fan J. 1991.. On the optimal rates of convergence for nonparametric deconvolution problems. . Ann. Stat. 19::1257–72
    [Google Scholar]
11. Fan J, Truong YK. 1993.. Nonparametric regression with errors in variables. . Ann. Stat. 21::1900–25
    [Google Scholar]
12. Frisch R. 1934.. Statistical Confluence Study. Oslo:: Univ. Inst. Econ.
13. Fuller W. 1987.. Measurement Error Models. New York:: Wiley
14. Jiang F, Ma Y. 2022.. Poisson regression with error corrupted high dimensional features. . Stat. Sin. 32:(4):2023–46
    [Google Scholar]
15. Jiang F, Ma Y, Carroll RJ. 2023.. A spline-assisted semiparametric approach to nonparametric measurement error models. . Econom. Stat. In press
    [Google Scholar]
16. Kress R. 1989.. Linear Integral Equations. New York:: Springer
17. Lederer W, Seibold H, Küchenhoff H, Lawrence C, Brøndum RF. 2019.. simex: SIMEX- and MCSIMEX-algorithm for measurement error models. . R Package, version 1.8. https://CRAN.R-project.org/package=simex
    [Google Scholar]
18. Li M, Li R, Ma Y. 2021.. Inference in high dimensional linear measurement error models. . J. Multivariate Anal. 184::104759
    [Crossref] [Google Scholar]
19. Liu MC, Taylor RL. 1989.. A consistent nonparametric density estimator for the deconvolution problem. . Can. J. Stat. 17::427–38
    [Crossref] [Google Scholar]
20. Loh PL, Wainwright M. 2012.. High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity. . Ann. Stat. 40::1637–64
    [Crossref] [Google Scholar]
21. Ma Y, Carroll RJ. 2006.. Locally efficient estimators for semiparametric models with measurement error. . J. Am. Stat. Assoc. 101::1465–74
    [Crossref] [Google Scholar]
22. Ma Y, Hart DJ. 2007.. Constrained local likelihood estimators for semiparametric skew-normal distributions. . Biometrika 94::119–34
    [Crossref] [Google Scholar]
23. Ma Y, Kim M, Genton MG. 2013.. Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias. . J. Am. Stat. Assoc. 108::1090–104
    [Crossref] [Google Scholar]
24. Ma Y, Tsiatis AA. 2006.. On closed form semiparametric estimators for measurement error models. . Stat. Sin. 16::183–93
    [Google Scholar]
25. Nab L, van Smeden M, Keogh RH, Groenwold RHH. 2021.. Mecor: an R package for measurement error correction in linear regression models with a continuous outcome. . Comput. Methods Programs Biomed. 208::106238
    [Crossref] [Google Scholar]
26. Powell J. 1984.. Least absolute deviations estimation for the censored regression model. . J. Econom. 28::303–25
    [Crossref] [Google Scholar]
27. Sarkar A, Mallick BK, Carroll RJ. 2014.. Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors. . Biometrics 70::823–34
    [Crossref] [Google Scholar]
28. Staudenmayer J, Ruppert D, Buonaccorsi JP. 2008.. Density estimation in the presence of heteroscedastic measurement error. . J. Am. Stat. Assoc. 103::726–36
    [Crossref] [Google Scholar]
29. Stefanski LA, Carroll RJ. 1985.. Covariate measurement error in logistic regression. . Biometrika 74::703–16
    [Google Scholar]
30. Stefanski LA, Carroll RJ. 1987.. Conditional scores and optimal scores for generalized linear measurement-error models. . Ann. Stat. 13::1335–51
    [Google Scholar]
31. Stefanski LA, Carroll RJ. 1990.. Deconvoluting kernel density estimators. . Statistics 21::169–84
    [Crossref] [Google Scholar]
32. Tsiatis AA, Ma Y. 2004.. Locally efficient semiparametric estimators for functional measurement error models. . Biometrika 91::835–48
    [Crossref] [Google Scholar]
33. Wang X, Wang B. 2011.. Deconvolution estimation in measurement error models: the R package decon. . J. Stat. Softw. 39:(10):1–24
    [Crossref] [Google Scholar]
34. Zhou H, Huang X. 2022.. lpme: Nonparametric estimation of measurement error models. . R Package, version 1.1.3. https://cran.r-project.org/package=lpme
    [Google Scholar]