2 citations · 4 across the 8 of their papers we have counts for
9 papers
Robust estimation in generalized linear models based on the normal quantiles of the probability integral transformation
Marina Valdora, Víctor Yohai
A new approach to robust estimation in generalized linear models is introduced. The idea of the method is to first transform the responses applying the composition of the normal qu…
Robust Penalized Estimators for High--Dimensional Generalized Linear Models
Marina Valdora, Claudio Agostinelli
Robust estimators for generalized linear models (GLMs) are not easy to develop due to the nature of the distributions involved. Recently, there has been growing interest in robust…
Robust estimation for functional logistic regression models
Graciela Boente, Marina Valdora
This paper addresses the problem of providing robust estimators under a functional logistic regression model. Logistic regression is a popular tool in classification problems with…
An unbiased estimator of the case fatality rate
Agustín Alvarez, Marina Fragalá, Marina Valdora
During an epidemic outbreak of a new disease, the probability of dying once infected is considered an important though difficult task to be computed. Since it is very hard to know…
Each student with her/his own data: understanding sampling distributions
Mariela Sued, Marina Valdora
Sampling distribution, a foundational concept in statistics, is difficult to understand, since we usually have only one realization of the estimator of interest. In this work, we p…
Robust Estimation in High Dimensional Generalized Linear Models
Marina Valdora, Claudio Agostinelli, Victor J. Yohai
Generalized Linear Models are routinely used in data analysis. The classical procedures for estimation are based on Maximum Likelihood and it is well known that the presence of out…
Robust Doubly Protected Estimators for Quantiles with Missing Data
Julieta Molina, Mariela Sued, Marina Valdora +1
Doubly protected estimators are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for th…
Models for the Propensity Score that Contemplate the Positivity Assumption and their Application to Missing Data and Causality
Julieta Molina, Mariela Sued, Marina Valdora
Generalized linear models are often assumed to fit propensity scores, which are used to compute inverse probability weighted (IPW) estimators. In order to derive the asymptotic pro…
Robust estimators for generalized linear models with a dispersion parameter
Michael Amiguet, Alfio Marazzi, Marina Valdora +1
Highly robust and efficient estimators for the generalized linear model with a dispersion parameter are proposed. The estimators are based on three steps. In the first step the max…