Robust statistics: a selective overview and new directions
M Avella Medina, E Ronchetti - Wiley Interdisciplinary Reviews …, 2015 - Wiley Online Library
Classical statistics relies largely on parametric models. Typically, assumptions are made on
the structural and the stochastic parts of the model and optimal procedures are derived …
the structural and the stochastic parts of the model and optimal procedures are derived …
Robust biased estimators for Poisson regression model: simulation and applications
The method of maximum likelihood flops when there is linear dependency (multicollinearity)
and outlier in the generalized linear models. In this study, we combined the ridge estimator …
and outlier in the generalized linear models. In this study, we combined the ridge estimator …
The differential association of COVID-19 remote digital instruction period with second-grade students' graphomotor, handwriting, visual, and sequential memory skills
R Ghanamah, H Eghbaria-Ghanamah… - Learning and …, 2024 - Elsevier
Background The COVID-19 pandemic has forced unexpected changes to ordinary societal
life, including the education system. Due to COVID-19 restrictions, multiple aspects of …
life, including the education system. Due to COVID-19 restrictions, multiple aspects of …
A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator
In this paper a robust version of the classical Wald test statistics for linear hypothesis in the
logistic regression model is introduced and its properties are explored. We study the …
logistic regression model is introduced and its properties are explored. We study the …
Robust mislabel logistic regression without modeling mislabel probabilities
H Hung, ZY Jou, SY Huang - Biometrics, 2018 - Wiley Online Library
Logistic regression is among the most widely used statistical methods for linear discriminant
analysis. In many applications, we only observe possibly mislabeled responses. Fitting a …
analysis. In many applications, we only observe possibly mislabeled responses. Fitting a …
[HTML][HTML] Influence analysis of robust Wald-type tests
We consider a robust version of the classical Wald test statistics for testing simple and
composite null hypotheses for general parametric models. These test statistics are based on …
composite null hypotheses for general parametric models. These test statistics are based on …
Robust inference for destructive one-shot device test data under Weibull lifetimes and competing risks
Data obtained from destructive one-shot devices are usually studied through a binary
response variable indicating failure or success of the device. But, in many practical …
response variable indicating failure or success of the device. But, in many practical …
Robust statistical inference in generalized linear models based on minimum Renyi's pseudodistance estimators
Minimum Renyi's pseudodistance estimators (MRPEs) enjoy good robustness properties
without a significant loss of efficiency in general statistical models, and, in particular, for …
without a significant loss of efficiency in general statistical models, and, in particular, for …
Robust inference and modeling of mean and dispersion for generalized linear models
Abstract Generalized Linear Models (GLMs) are a popular class of regression models when
the responses follow a distribution in the exponential family. In real data the variability often …
the responses follow a distribution in the exponential family. In real data the variability often …
[HTML][HTML] Robust polytomous logistic regression
In the context of polytomous regression, as with any generalized linear model, robustness
issues are well documented. Existing robust estimators are designed to protect against …
issues are well documented. Existing robust estimators are designed to protect against …