
Robust estimation for semifunctional linear regression models
Semifunctional linear regression models postulate a linear relationship...
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A robust approach for ROC curves with covariates
The Receiver Operating Characteristic (ROC) curve is a useful tool that ...
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Individual Heterogeneity Learning in Distributional Data Response Additive Models
In many complex applications, data heterogeneity and homogeneity exist s...
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Sparse Model Identification and Learning for Ultrahighdimensional Additive Partially Linear Models
The additive partially linear model (APLM) combines the flexibility of n...
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Statistical Inference for Generalized Additive Partially Linear Model
The Generalized Additive Model (GAM) is a powerful tool and has been wel...
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Fast Bayesian Inference in Nonparametric Double Additive LocationScale Models With Right and IntervalCensored Data
Penalized Bsplines are routinely used in additive models to describe sm...
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On doubly robust estimation for logistic partially linear models
Consider a logistic partially linear model, in which the logit of the me...
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A robust spline approach in partially linear additive models
Partially linear additive models generalize the linear models since they model the relation between a response variable and covariates by assuming that some covariates are supposed to have a linear relation with the response but each of the others enter with unknown univariate smooth functions. The harmful effect of outliers either in the residuals or in the covariates involved in the linear component has been described in the situation of partially linear models, that is, when only one nonparametric component is involved in the model. When dealing with additive components, the problem of providing reliable estimators when atypical data arise, is of practical importance motivating the need of robust procedures. Hence, we propose a family of robust estimators for partially linear additive models by combining Bsplines with robust linear regression estimators. We obtain consistency results, rates of convergence and asymptotic normality for the linear components, under mild assumptions. A Monte Carlo study is carried out to compare the performance of the robust proposal with its classical counterpart under different models and contamination schemes. The numerical experiments show the advantage of the proposed methodology for finite samples. We also illustrate the usefulness of the proposed approach on a real data set.
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