Papers by daniela rodriguez
Journal of Mathematical Imaging and Vision, 2009
The paper concerns the strong uniform consistency and the asymptotic distribution of the kernel d... more The paper concerns the strong uniform consistency and the asymptotic distribution of the kernel density estimator of random objects on a Riemannian manifolds, proposed by Pelletier (Stat. Probab. Lett., 73(3):297–304, 2005). The estimator is illustrated via one example based on a real data.
In partially linear models the dependence of the response y on (x^T,t) is modeled through the rel... more In partially linear models the dependence of the response y on (x^T,t) is modeled through the relationship y=\x^T \beta+g(t)+\epsilon where \epsilon is independent of (x^T,t). In this paper, estimators of \beta and g are constructed when the explanatory variables t take values on a Riemannian manifold. Our proposal combine the flexibility of these models with the complex structure of a set of explanatory variables. We prove that the resulting estimator of \beta is asymptotically normal under the suitable conditions. Through a simulation study, we explored the performance of the estimators. Finally, we applied the studied model to an example based on real dataset.
Under a partially linear models we study a family of robust estimates for the regression paramete... more Under a partially linear models we study a family of robust estimates for the regression parameter and the regression function when some of the predictor variables take values on a Riemannian manifold. We obtain the consistency and the asymptotic normality of the proposed estimators. Also, we consider a robust cross validation procedure to select the smoothing parameter. Simulations and application to real data show the performance of our proposal under small samples and contamination.
Partly linear models on Riemannian manifolds
Journal of Applied Statistics, 2012
In partly linear models, the dependence of the response y on (x T, t) is modeled through the rela... more In partly linear models, the dependence of the response y on (x T, t) is modeled through the relationship y=x Tβ+g(t)+ϵ, where ϵ is independent of (x T, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO2 pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variables t take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.
Journal of Nonparametric Statistics, 2009
In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables b... more In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belong in a Riemannian manifolds. We study asymptotic properties such as the consistency and the asymptotic distribution. A simulation study is also consider to evaluate the performance of the proposal. Finally, to illustrate the potential applications of the proposed estimator, we analyzed two real example where two different manifolds are considered.
Pediatric Emergency Care, 2010
Objectives: To develop a mathematical model to predict the probability of having community-acquir... more Objectives: To develop a mathematical model to predict the probability of having community-acquired pneumonia and to evaluate an already developed prediction rule that has not been validated in a clinical scenario.
Pediatric Emergency Care, 2010
Objectives: To develop a mathematical model to predict the probability of having community-acquir... more Objectives: To develop a mathematical model to predict the probability of having community-acquired pneumonia and to evaluate an already developed prediction rule that has not been validated in a clinical scenario.
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Papers by daniela rodriguez