Estimating additive models with missing responses

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For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed that we observe all the data. However, in many applied statistical analysis mi...

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Autor principal: Boente, G.
Otros Autores: Martínez, A.M
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: Taylor and Francis Inc. 2016
Acceso en línea:Registro en Scopus
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Biblioteca Central Dr. Luis F. Leloir (FCEN) de Universidad de Buenos Aires
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100 1 |a Boente, G. 
245 1 0 |a Estimating additive models with missing responses 
260 |b Taylor and Francis Inc.  |c 2016 
270 1 0 |m Boente, G.; IMAS, CONICET, Departamento de Matemáticas, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 1, Argentina; email: gboente@dm.uba.ar 
506 |2 openaire  |e Política editorial 
504 |a Aerts, M., Claeskens, G., Hens, N., Molenberghs, G., Local multiple imputation (2002) Biometrika, 89 (2), pp. 375-388 
504 |a Boente, G., Martínez, A., (2012) Estimating Additive Models with Missing Responses, , www.ic.fcen.uba.ar/preprints/Paper_aditivo_Boente_Martinez.pdf 
504 |a Buja, A., Hastie, T., Tibshirani, R., Linear smoothers and additive models (with discussion) (1989) Ann. Stat., 17, pp. 453-555 
504 |a Chen, J.H., Shao, J., Nearest neighbor imputation for survey data (2000) J. Off. Stat., 16, pp. 113-131 
504 |a Cheng, P.E., Applications of kernel regression estimation: A survey (1990) Commun. Stat. Ser. A, Theory Methods, 19, pp. 4103-4134 
504 |a Cheng, P.E., Nonparametric estimation of mean functionals with data missing at random (1994) J. Am. Stat. Assoc., 89, pp. 81-87 
504 |a Cheng, P.E., Wei, L.J., Nonparametric inference under ignorable missing data process and treatment assignment (1986) Int. Stat. Symposium, Taipei, ROC, 1, pp. 97-112 
504 |a Chu, C.K., Cheng, P.E., Nonparametric regression estimation with missing data (1995) J. Stat. Plan. Inference, 48, pp. 85-99 
504 |a Devroye, L.P., The uniform convergence of the Nadaraya-Watson regression function estimate (1978) Can. J. Stat., 6, pp. 179-191 
504 |a González-Manteiga, W., Pérez-González, A., Nonparametric mean estimation with missing data (2004) Commun. Stat. Theory Methods, 33, pp. 277-303 
504 |a Härdle, W., Müller, M., Sperlich, S., Werwatz, A., Nonparametric and Semiparametric Models (2004) Springer Series in Statistics, , Berlin: Springer 
504 |a Hastie, T.J., Tibshirani, R.J., (1990) Generalized Additive Models, , London: Chapman and Hall 
504 |a Hengartner, N.W., Sperlich, S., Rate optimal estimation with the integration method in the presence of many covariates (2005) J. Multivar. Anal, 95, pp. 246-272 
504 |a Hirano, K., Imbens, G., Ridder, G., Efficient Estimation of Average Treatment Effects using the Estimated Propensity Score (2000) NBER Technical Working Paper 251 
504 |a Koul, H.L., Muüller, U.U., Schick, A., The Transfer Principle: A tool for complete case analysis (2012) Ann. Stat., 40, pp. 3031-3049 
504 |a Linton, O.B., Härdle, W., Estimation of additive regression models with known links (1996) Biometrika, 83, pp. 529-540 
504 |a Linton, O.B., Nielsen, J.P., A kernel method of estimating structured nonparametric regression based on marginal integration (1995) Biometrika, 82, pp. 93-101 
504 |a Mammen, E., Park, C., Bandwidth selection for smooth backfitting in additive models (2005) The Annals of Statistics, 33, pp. 1260-1294 
504 |a Mammen, E., Linton, O., Nielsen, J.P., The existence and asymptotic properties of a backfitting projection algorithm under weak conditions (1999) Ann. Stat., 27, pp. 1443-1490 
504 |a Martínez-Miranda, M.D., Raya-Miranda, R., González-Manteiga, W., González-Carmona, A., A bootstrap local bandwidth selector for additive models (2008) J. Comput. Graph. Stat., 17, pp. 38-55 
504 |a Martínez-Miranda, M.D., Raya-Miranda, R., Data-driven local bandwidth selection for additive models with missing data (2011) Appl. Math. Comput., 217, pp. 10328-10342 
504 |a Nadaraya, E.A., On estimating regression (1964) Theory Prob. Appl., 9, pp. 141-142 
504 |a Neyman, J., Contribution to the theory of sampling human populations (1938) J. Am. Stat. Assoc., 33, pp. 101-116 
504 |a Newey, W.K., Kernel estimation of partial means (1994) Econ. Theory, 10, pp. 233-253 
504 |a Nielsen, J.P., Sperlich, S., Smooth backfitting in practise (2005) Journal of the Royal Statistical Society, Ser. B, 67, pp. 43-61 
504 |a Opsomer, J.D., Asymptotic properties of backfitting estimators (2000) J. Multivar. Anal., 73, pp. 166-179 
504 |a Prakasa Rao, B.L.S., (1983) Nonparametric Functional Estimation, , London: Academic Press 
504 |a Stone, C.J., The dimensionality reduction principle for generalized additive models (1986) Ann. Statist., 14, pp. 590-606 
504 |a Tjostheim, D., Auestad, B.H., Nonparametric identification of nonlinear time series: Projections (1994) J. Am. Stat. Assoc., 89, pp. 1398-1409 
504 |a Wang, Q., Linton, O., Härdle, W., Semiparametric regression analysis with missing response at random (2004) J. Am. Stat. Assoc., 99 (466), pp. 334-345 
504 |a Wang, W., Rao, J.N.K., Empirical likelihood-based inference under imputation for missing response data (2002) Ann. Stat., 30, pp. 896-924 
504 |a Watson, G.S., Smooth regression analysis (1964) Sankhya¯ A, 26, pp. 359-372 
520 3 |a For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed that we observe all the data. However, in many applied statistical analysis missing data occur. In this paper, we study the effect of missing responses on the additive components estimation. The estimators are based on marginal integration adapted to the missing situation. The proposed estimators turn out to be consistent under mild assumptions. A simulation study allows to compare the behavior of our procedures, under different scenarios. © 2016 Taylor & Francis Group, LLC.  |l eng 
593 |a IMAS, CONICET, Departamento de Matemáticas, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 1, Buenos Aires, C1428EHA, Argentina 
690 1 0 |a ADDITIVE MODELS 
690 1 0 |a KERNEL WEIGHTS 
690 1 0 |a MARGINAL INTEGRATION 
690 1 0 |a MISSING DATA 
690 1 0 |a NON PARAMETRIC REGRESSION 
690 1 0 |a STATISTICAL METHODS 
690 1 0 |a STATISTICS 
690 1 0 |a ADDITIVE MODELS 
690 1 0 |a KERNEL WEIGHT 
690 1 0 |a MARGINAL INTEGRATION 
690 1 0 |a MISSING DATA 
690 1 0 |a NON-PARAMETRIC REGRESSION 
690 1 0 |a ESTIMATION 
700 1 |a Martínez, A.M. 
773 0 |d Taylor and Francis Inc., 2016  |g v. 45  |h pp. 413-429  |k n. 2  |p Commun Stat Theory Methods  |x 03610926  |w (AR-BaUEN)CENRE-84  |t Communications in Statistics - Theory and Methods 
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856 4 0 |u https://doi.org/10.1080/03610926.2013.815780  |y DOI 
856 4 0 |u https://hdl.handle.net/20.500.12110/paper_03610926_v45_n2_p413_Boente  |y Handle 
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