Robust location estimation with missing data
In a missing data setting, we have a sample in which a vector of explanatory variables xi is observed for every subject i, while scalar responses yi are missing by happenstance on some individuals. In this work we propose robust estimators of the distribution of the responses assuming missing at ran...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
2013
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 07084caa a22006377a 4500 | ||
|---|---|---|---|
| 001 | PAPER-11727 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518204154.0 | ||
| 008 | 190411s2013 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-84873995042 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Sued, M. | |
| 245 | 1 | 0 | |a Robust location estimation with missing data |
| 260 | |c 2013 | ||
| 270 | 1 | 0 | |m Sued, M.; Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires and CONICET, Buenos Aires, Argentina; email: msued@dm.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Bang, H., Robins, J., Doubly robust estimation in missing data and causal inference models (2005) Biometrics, 61, pp. 962-972 | ||
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| 504 | |a Boente, G., Rodriguez, D., Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis (2008) Computational Statistics and Data Analysis, 52, pp. 2808-2828 | ||
| 504 | |a Boente, G., González-Manteiga, W., Pérez-González, A., Robust nonparametric estimation with missing data (2009) Journal of Statistical Planning and Inference, 139, pp. 571-592 | ||
| 504 | |a Dehejia, R.H., Wahba, S., Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs (1999) Journal of the American Statistical Association, 94, pp. 1053-1062 | ||
| 504 | |a Donoho, D.L., Huber, P.J., The notion of breakdown point (1983) A Festschrift for E. L. Lehmann, pp. 157-184. , Bickel, P. J., Doksum, K. A., & Hodges, J. L., editors. Wadsworth, Belmont, CA | ||
| 504 | |a Fasano, M.V., (2009), http://www.mate.unlp.edu.ar/tesis/tesis_fasano_v.pdf, Robust estimation in nonlinear regression. Ph.D. Thesis, University of La Plata. Available at; Fasano, M.V., Maronna, R.A., Sued, M., Yohai, V.J., Continuity and differentiability of regression M functionals (2011) Bernoulli, , in press) | ||
| 504 | |a Hampel, F.R., The influence curve and its role in robust estimation (1974) Journal of the American Statistical Association, 69, pp. 383-393 | ||
| 504 | |a Hössjer, O., On the optimality of S-estimators (1992) Statistics and Probability Letters, 14, pp. 413-419 | ||
| 504 | |a Huber, P.J., Robust estimation of a location parameter (1964) The Annals of Mathematical Statistics, 35, pp. 73-101 | ||
| 504 | |a Huber, P.J., Ronchetti, E.M., (2009) Robust Statistics, , 2nd ed., Wiley, New York | ||
| 504 | |a Hubert, M., Engelen, S., Fast cross-validation of high-breakdown resampling algorithms for PCA (2007) Computational Statistics and Data Analysis, 51, pp. 5013-5024 | ||
| 504 | |a Kang, J.D.Y., Schafer, J.L., Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data (2007) Statistical Science, 22, pp. 523-539 | ||
| 504 | |a Khan, J.A., Van Aelst, S., Zamar, R.H., Robust linear model selection based on least angle regression (2007) Journal of the American Statistical Association, 102, pp. 1289-1299 | ||
| 504 | |a LaLonde, R.J., Evaluating the econometric evaluations of training programs with experimental data (1986) American Economic Review, 76, pp. 604-620 | ||
| 504 | |a Maronna, R.A., Martin, R.D., Yohai, V.J., (2006) Robust Statistics: Theory and Methods, , Wiley, Chichester | ||
| 504 | |a Müler, U.U., Estimating linear functionals in nonlinear regression with responses missing at random (2009) Annals of Statistics, 37, pp. 2245-2277 | ||
| 504 | |a Robins, J., Sued, M., Lei-Gomez, Q., Rotnitzky, A., Comment: Performance of double-robust estimators when inverse probability weights are highly variable (2007) Statistical Science, 22, pp. 544-559 | ||
| 504 | |a Ronchetti, E., Field, R., Blanchard, W., Robust linear model selection by cross-validation (1997) Journal of the American Statistical Association, 92, pp. 1017-1023 | ||
| 504 | |a Ronchetti, E., Staudte, R.G., A robust version of Mallows's Cp (1994) Journal of the American Statistical Association, 89, pp. 550-559 | ||
| 504 | |a Rousseeuw, P.J., Yohai, V.J., Robust regression by means of S-estimators (1984) Robust and Nonlinear Time Series, 26, pp. 256-272. , Franke, J., Hardle, W., & Martin, R. D., editors. Lecture Notes in Statistics, Springer, New York | ||
| 504 | |a Rubin, D., Inference and missing data (1976) Biometrika, 63, pp. 581-592 | ||
| 504 | |a Yohai, V.J., High breakdown-point and high efficiency estimates for regression (1987) The Annals of Statistics, 15, pp. 642-656 | ||
| 520 | 3 | |a In a missing data setting, we have a sample in which a vector of explanatory variables xi is observed for every subject i, while scalar responses yi are missing by happenstance on some individuals. In this work we propose robust estimators of the distribution of the responses assuming missing at random (MAR) data, under a semiparametric regression model. Our approach allows the consistent estimation of any weakly continuous functional of the response's distribution. In particular, strongly consistent estimators of any continuous location functional, such as the median, L-functionals and M-functionals, are proposed. A robust fit for the regression model combined with the robust properties of the location functional gives rise to a robust recipe for estimating the location parameter. Robustness is quantified through the breakdown point of the proposed procedure. The asymptotic distribution of the location estimators is also derived. The proofs of the theorems are presented in Supplementary Material available online. © 2012 Statistical Society of Canada. |l eng | |
| 593 | |a Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires and CONICET, Buenos Aires, Argentina | ||
| 593 | |a Departamento de Matemáticas, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires and CONICET, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a ASYMPTOTIC DISTRIBUTION |
| 690 | 1 | 0 | |a BREAKDOWN POINT |
| 690 | 1 | 0 | |a L-LOCATION FUNCTIONAL |
| 690 | 1 | 0 | |a M-LOCATION FUNCTIONAL |
| 690 | 1 | 0 | |a MEDIAN |
| 690 | 1 | 0 | |a MISSING AT RANDOM |
| 700 | 1 | |a Yohai, V.J. | |
| 773 | 0 | |d 2013 |g v. 41 |h pp. 111-132 |k n. 1 |p Can. J. Stat. |x 03195724 |t Canadian Journal of Statistics | |
| 856 | 4 | 1 | |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873995042&doi=10.1002%2fcjs.11163&partnerID=40&md5=e7e2e717264341e47bb47533094c4b5e |y Registro en Scopus |
| 856 | 4 | 0 | |u https://doi.org/10.1002/cjs.11163 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_03195724_v41_n1_p111_Sued |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03195724_v41_n1_p111_Sued |y Registro en la Biblioteca Digital |
| 961 | |a paper_03195724_v41_n1_p111_Sued |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/article |a info:ar-repo/semantics/artículo |b info:eu-repo/semantics/publishedVersion | ||