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pro vyhledávání: '"Castilla, Elena"'
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
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
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
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
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
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
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
Autor:
Castilla, Elena1 (AUTHOR) elena.castilla@urjc.es, Ghosh, Abhik2 (AUTHOR) abhik.ghosh@isical.ac.in
Publikováno v:
Entropy. Oct2023, Vol. 25 Issue 10, p1422. 16p.
Publikováno v:
In Journal of Multivariate Analysis March 2022 188
A new family of minimum distance estimators for binary logistic regression models based on $\phi$-divergence measures is introduced. The so called "pseudo minimum phi-divergence estimator"(PM$\phi$E) family is presented as an extension of "minimum ph
Externí odkaz:
http://arxiv.org/abs/1611.02583