Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Bianco, Ana M."'
Functional markers become a more frequent tool in medical diagnosis. In this paper, we aim to define an index allowing to discriminate between populations when the observations are functional data belonging to a Hilbert space. We discuss some of the
Externí odkaz:
http://arxiv.org/abs/2407.20929
Autor:
Bianco, Ana M., Boente, Graciela, González--Manteiga, Wenceslao, Sampedro, Francisco Gude, Pérez--González, Ana
Statistical analysis on compositional data has gained a lot of attention due to their great potential of applications. A feature of these data is that they are multivariate vectors that lie in the simplex, that is, the components of each vector are p
Externí odkaz:
http://arxiv.org/abs/2405.12924
We collect robust proposals given in the field of regression models with heteroscedastic errors. Our motivation stems from the fact that the practitioner frequently faces the confluence of two phenomena in the context of data analysis: non--linearity
Externí odkaz:
http://arxiv.org/abs/2311.02822
Penalized $M-$estimators for logistic regression models have been previously study for fixed dimension in order to obtain sparse statistical models and automatic variable selection. In this paper, we derive asymptotic results for penalized $M-$estima
Externí odkaz:
http://arxiv.org/abs/2201.12449
In this paper, we present three estimators of the ROC curve when missing observations arise among the biomarkers. Two of the procedures assume that we have covariates that allow to estimate the propensity and the estimators are obtained using an inve
Externí odkaz:
http://arxiv.org/abs/2201.06483
The Receiver Operating Characteristic (ROC) curve is a useful tool that measures the discriminating power of a continuous variable or the accuracy of a pharmaceutical or medical test to distinguish between two conditions or classes. In certain situat
Externí odkaz:
http://arxiv.org/abs/2007.00150
This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some of covariates. The target is a marginal location parameter which is given through an $M-$functional. To obtain
Externí odkaz:
http://arxiv.org/abs/2005.03511
Sparse covariates are frequent in classification and regression problems and in these settings the task of variable selection is usually of interest. As it is well known, sparse statistical models correspond to situations where there are only a small
Externí odkaz:
http://arxiv.org/abs/1911.00554
In this paper, we propose a robust profile estimation method for the parametric and nonparametric components of a single index model when the errors have a strongly unimodal density with unknown nuisance parameter. Under regularity conditions, we der
Externí odkaz:
http://arxiv.org/abs/1709.05422