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A neural system to robust Nonlinear optimization subject to disjoint and constrained sets
(Int Inst Informatics & Systemics, 2001-01-01)
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter ...
A neural system to robust Nonlinear optimization subject to disjoint and constrained sets
(Int Inst Informatics & Systemics, 2001-01-01)
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter ...
Robust estimators in semi-functional partial linear regression models
(Elsevier Inc, 2017-02)
Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the ...
On a robust local estimator for the scale function in heteroscedastic nonparametric regression
(Elsevier Science, 2010-08)
When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function ...
Multiple robust estimation of marginal structural mean models for unconstrained outcomes
(Wiley Blackwell Publishing, Inc, 2019-03)
We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double ...
Robust estimators in partly linear regression models on Riemannian manifolds
(Taylor, 2014)
Under a partly linear model we study a family of robust estimates for the regression parameter and the regression function when some of the predictors take values on a Riemannian manifold. We obtain the consistency and the ...
Robust inference in generalized partially linear models
(Elsevier Science, 2010-12)
In many situations, data follow a generalized partly linear model in which the mean of the responses is modeled, through a link function, linearly on some covariates and nonparametrically on the remaining ones. A new class ...
Influence diagnostics for elliptical semiparametric mixed models
(SAGE PUBLICATIONS LTD, 2012)
In this paper we extend semiparametric mixed linear models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. Penalized likelihood equations are ...
Robust estimators for additive models using backfitting
(Taylor & Francis Ltd, 2017-10)
Additive models provide an attractive setup to estimate regression functions in a nonparametric context. They provide a flexible and interpretable model, where each regression function depends only on a single explanatory ...
Efficient and robust state estimation: Application to a copolymerization process
(John Wiley & Sons Inc., 2020-11)
Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance ...