Výsledky vyhledávání - "Castilla, Elena"

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A new robust approach for the polytomous logistic regression model based on R\'enyi's pseudodistances

Autor: Castilla, Elena

This paper presents a robust alternative to the Maximum Likelihood Estimator (MLE) for the Polytomous Logistic Regression Model (PLRM), known as the family of minimum R\`enyi Pseudodistance (RP) estimators. The proposed minimum RP estimators are para

Externí odkaz: http://arxiv.org/abs/2402.02867

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Robust inference for non-destructive one-shot device testing under step-stress model with exponential lifetimes

Autor: Balakrishnan, Narayanaswamy, Castilla, Elena, Jaenada, María, Pardo, Leandro

One-shot devices analysis involves an extreme case of interval censoring, wherein one can only know whether the failure time is either before or after the test time. Some kind of one-shot devices do not get destroyed when tested, and so can continue

Externí odkaz: http://arxiv.org/abs/2204.11560

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Robust approach for comparing two dependent normal populations through Wald-type tests based on R\'enyi's pseudodistance estimators

Autor: Jaenada, María, Castilla, Elena, Martín, Nirian, Pardo, Leandro

Since the two seminal papers by Fisher (1915, 1921) were published, the test under a fixed value correlation coefficient null hypothesis for the bivariate normal distribution constitutes an important statistical problem. In the framework of asymptoti

Externí odkaz: http://arxiv.org/abs/2202.00982

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Estimation and testing on independent not identically distributed observations based on R\'enyi's pseudodistances

Autor: Castilla, Elena, Jaenada, Maria, Pardo, Leandro

In real life we often deal with independent but not identically distributed observations (i.n.i.d.o), for which the most well-known statistical model is the multiple linear regression model (MLRM) without random covariates. While the classical method

Externí odkaz: http://arxiv.org/abs/2102.12282

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A new robust approach for multinomial logistic regression with complex design model

Autor: Castilla, Elena, Chocano, Pedro J.

Robust estimators and Wald-type tests are developed for the multinomial logistic regression based on $\phi$-divergence measures. The robustness of the proposed estimators and tests is proved through the study of their influence functions and it is al

Externí odkaz: http://arxiv.org/abs/2102.03073

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On regularization methods based on R\'enyi's pseudodistances for sparse high-dimensional linear regression models

Autor: Castilla, Elena, Ghosh, Abhik, Jaenada, María, Pardo, Leandro

Several regularization methods have been considered over the last decade for sparse high-dimensional linear regression models, but the most common ones use the least square (quadratic) or likelihood loss and hence are not robust against data contamin

Externí odkaz: http://arxiv.org/abs/2007.15929

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Robust semiparametric inference for polytomous logistic regression with complex survey design

Autor: Castilla, Elena, Ghosh, Abhik, Martin, Nirian, Pardo, Leandro

Analyzing polytomous response from a complex survey scheme, like stratified or cluster sampling is very crucial in several socio-economics applications. We present a class of minimum quasi weighted density power divergence estimators for the polytomo

Externí odkaz: http://arxiv.org/abs/1904.02219

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