Robust estimation in partially linear errors-in-variables models
In many applications of regression analysis, there are covariates that are measured with errors. A robust family of estimators of the parametric and nonparametric components of a structural partially linear errors-in-variables model is introduced. The proposed estimators are based on a three-step pr...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier B.V.
2017
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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 | 07830caa a22007577a 4500 | ||
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| 001 | PAPER-15224 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518204554.0 | ||
| 008 | 190410s2017 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-84988591402 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 030 | |a CSDAD | ||
| 100 | 1 | |a Bianco, A.M. | |
| 245 | 1 | 0 | |a Robust estimation in partially linear errors-in-variables models |
| 260 | |b Elsevier B.V. |c 2017 | ||
| 270 | 1 | 0 | |m Bianco, A.M.; Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Piso 2, Argentina; email: abianco@dm.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
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| 504 | |a Bianco, A., Boente, G., Robust estimators under semi-parametric partly linear autoregression: Asymptotic behaviour and bandwidth selection (2007) J. Time Series Anal., 28, pp. 274-306 | ||
| 504 | |a Boente, G., Fraiman, R., Robust nonparametric regression estimation for dependent observations (1990) Ann. Statist., 17, pp. 1242-1256 | ||
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| 504 | |a Boente, G., Rodriguez, D., Robust inference in generalized partially linear models (2010) Comput. Statist. Data Anal., 54, pp. 2942-2966 | ||
| 504 | |a Buonaccorsi, J.P., Measurement Error: Models, Methods and Applications (2010), Chapman and Hall /CRC USA; Carroll, R.J., Ruppert, D., Stefanski, L.A., Measurement Error in Nonlinear Models (1995), Chapman and Hall London; Croux, C., Fekri, M., Ruiz-Gazen, A., Fast and robust estimation of the multivariate errors in variables model (2010) TEST, 19, pp. 286-303 | ||
| 504 | |a Cui, H., Kong, E., Empirical likelihood confidence region for parameters in semi-linear errors-in-variables models (2006) Scand. J. Stat., 33, pp. 153-168 | ||
| 504 | |a Fekri, M., Ruiz-Gazen, A., Robust weighted orthogonal regression in the errors-in-variables model (2004) J. Multivariate Anal., 88, pp. 89-108 | ||
| 504 | |a Fuller, W.A., Measurement Error Models (1987), Wiley New York; Hampel, F.R., The influence curve and its role in robust estimation (1974) J. Amer. Statist. Assoc., 69, pp. 383-394 | ||
| 504 | |a Härdle, W., Robust regression function estimation (1984) J. Multivariate Anal., 14, pp. 169-180 | ||
| 504 | |a Härdle, W., Liang, H., Gao, J., Partially Linear Models (2000), Physica–Verlag; He, X., Liang, H., Quantile regression estimates for a class of linear and partially linear errorsin-variables models (2000) Statist. Sinica, 10, pp. 129-140 | ||
| 504 | |a He, X., Zhu, Z., Fung, W., Estimation in a semiparametric model for longitudinal data with unspecified dependence structure (2002) Biometrika, 89, pp. 579-590 | ||
| 504 | |a Liang, H., Asymptotic normality of parametric part in partially linear model with measurement error in the non-parametric part (2000) J. Statist. Plann. Inference, 86, pp. 51-62 | ||
| 504 | |a Liang, H., Härdle, W., Carroll, R.J., Estimation in a semiparametric partially linear errors–in–variables model (1999) Ann. Statist., 27, pp. 1519-1535 | ||
| 504 | |a Liang, H., Li, R., Variable selection for partially linear models with measurement errors (2009) J. Amer. Statist. Assoc., 104, pp. 234-248 | ||
| 504 | |a Liang, H., Wang, S., Carroll, R.J., Partially linear models with missing response variables and error-prone covariates (2007) Biometrika, 94, pp. 185-198 | ||
| 504 | |a Ma, Y., Carroll, R.J., Locally efficient estimators for semiparametric models with measurement error (2006) J. Amer. Statist. Assoc., 101, pp. 1465-1474 | ||
| 504 | |a Mallows, C., On some topics in robustness (1974), Memorandum, Bell Laboratories, Murray Hill, NJ; Manchester, L., Empirical influence for robust smoothing (1996) Austral. J. Statist., 38, pp. 275-296 | ||
| 504 | |a Maronna, R., Yohai, V.J., Correcting MM-estimates for fat data sets (2010) J. Comput. Statist. Data Anal., 54, pp. 3168-3173 | ||
| 504 | |a Pan, W., Zeng, D., Lin, X., Estimation in semiparametric transition measurement error models for longitudinal data (2008) Biometrics, 65, pp. 728-736 | ||
| 504 | |a Severini, T., Staniswalis, J., Quasi-likelihood estimation in semiparametric models (1994) J. Amer. Statist. Assoc., 89, pp. 501-511 | ||
| 504 | |a Tamine, J., Smoothed influence function: another view at robust nonparametric regression (2002), Discussion paper 62, Sonderforschungsbereich 373, Humboldt-Universitat zu Berlin; Tukey, J., Exploratory Data Analysis (1977), Addison-Wesley Reading, MA; Zamar, R., Robust estimation in the errors–in–variables model (1989) Biometrika, 76, pp. 149-160 | ||
| 504 | |a Zhu, L.X., Cui, H.J., A semi-parametric regression model with errors in the variables (2003) Scand. J. Stat., 30, pp. 429-442 | ||
| 520 | 3 | |a In many applications of regression analysis, there are covariates that are measured with errors. A robust family of estimators of the parametric and nonparametric components of a structural partially linear errors-in-variables model is introduced. The proposed estimators are based on a three-step procedure where robust orthogonal regression estimators are combined with robust smoothing techniques. Under regularity conditions, it is proved that the resulting estimators are consistent. The robustness of the proposal is studied by means of the empirical influence function when the linear parameter is estimated using the orthogonal M-estimator. A simulation study allows to compare the behaviour of the robust estimators with their classical relatives and a real example data is analysed to illustrate the performance of the proposal. © 2016 Elsevier B.V. |l eng | |
| 593 | |a Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Ciudad Universitaria, Buenos Aires, Argentina | ||
| 593 | |a Departamento de Ciencias Exactas, Ciclo Básico Común, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a FISHER-CONSISTENCY |
| 690 | 1 | 0 | |a KERNEL WEIGHTS |
| 690 | 1 | 0 | |a M-LOCATION FUNCTIONALS |
| 690 | 1 | 0 | |a NONPARAMETRIC REGRESSION |
| 690 | 1 | 0 | |a ROBUST ESTIMATION |
| 690 | 1 | 0 | |a ERRORS |
| 690 | 1 | 0 | |a ORTHOGONAL FUNCTIONS |
| 690 | 1 | 0 | |a REGRESSION ANALYSIS |
| 690 | 1 | 0 | |a FISHER-CONSISTENCY |
| 690 | 1 | 0 | |a FUNCTIONALS |
| 690 | 1 | 0 | |a KERNEL WEIGHT |
| 690 | 1 | 0 | |a NON-PARAMETRIC REGRESSION |
| 690 | 1 | 0 | |a ROBUST ESTIMATION |
| 690 | 1 | 0 | |a PARAMETER ESTIMATION |
| 700 | 1 | |a Spano, P.M. | |
| 773 | 0 | |d Elsevier B.V., 2017 |g v. 106 |h pp. 46-64 |p Comput. Stat. Data Anal. |x 01679473 |w (AR-BaUEN)CENRE-4276 |t Computational Statistics and Data Analysis | |
| 856 | 4 | 1 | |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988591402&doi=10.1016%2fj.csda.2016.09.002&partnerID=40&md5=4da31cc0c533d8a3969aeff42b1e7ea0 |y Registro en Scopus |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.csda.2016.09.002 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_01679473_v106_n_p46_Bianco |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v106_n_p46_Bianco |y Registro en la Biblioteca Digital |
| 961 | |a paper_01679473_v106_n_p46_Bianco |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/article |a info:ar-repo/semantics/artículo |b info:eu-repo/semantics/publishedVersion | ||
| 999 | |c 76177 | ||