Robust Inference for Generalized Linear Models

By Eva Cantoni, Elvezio Ronchetti. 2001

385 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a09cd6b3d137dd96fd3b7e6cf504c5f8570fd3ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Robust estimates in generalized partially linear models (2006, methodology): “Then easy calculations show that the minimizer â of Sn(a) = n −1∑n i=1 ρ(yi, a) equals the classical estimator, that is, â=H−1( ∑n i=1 yi/n) with H(u) = 1/(1 + exp(−u)), when using either the score function proposed in [5] or that given by Cantoni and Ronchetti [9].”
Robust estimation in generalized linear models: the density power divergence approach (2014, methodology): “The data were also analyzed by Hosseinian (2009) who compared the maximum likelihood estimator with the robust methodologies proposed by herself in the same paper and those by Cantoni and Ronchetti (2001).”
Review of Statistical Methods for Analysing Healthcare Resources and Costs (2010, background): “Furthermore in the case of unknown separation (latent mixture models) and more than two components the identification of all components might be difficult because of the increasing overlap of distributions (Cameron and Trivedi, 2009).”
Robust estimation for linear regression with asymmetric errors (2005, methodology): “More recently, Cantoni and Ronchetti (2001) derived a class of robust estimates based on the notion of quasi-likelihood. All these estimates require the computation of a correction term in order to make the estimates Fisher-consistent. In this paper we generalize the MM-estimates for the case of err”
Robust Estimation in Generalized Partial Linear Models for Clustered Data (2005, methodology): “Therefore, robust methods that limit the influence of outliers are desirable, as demonstrated by Preisser and Qaqish (1999) and Cantoni and Ronchetti (2001).”
Adaptively truncated maximum likelihood regression with asymmetric errors (2004, background): “Recent research in parametric robust estimation (Hampel et al., 1986) pays attention to univariate asymmetric models (e.g., Victoria-Feser and Ronchetti, 1994, 1997; Marazzi and Ruffieux, 1996, 1999) as well as to asymmetric response regression (e.g., Cantoni and Ronchetti, 2001 ).”
Robust Estimation Using the Huber Function With a Data-Dependent Tuning Constant (2007, methodology): “Other values are also used in the literature, for example, c = 1.2 (Cantoni and Ronchetti 2001), c = 1.25 (Chi 1994; Street, Carroll, and Ruppert 1988).”
Analysis of Robust Quasi-deviances for Generalized Linear Models (2004, methodology): “Cantoni and Ronchetti (2001) suggest the following set of estimating equations for the robust estimation of β: n∑ i=1 ψ(yi, µi) = 0, (2) where ψ(y, µ) = ν(y, µ)w(x)µ′ − a(β), a(β) = 1 n ∑n i=1 E[ν(yi, µi)]w(xi)µ ′ i with the expectation taken with respect to the conditional distribution of y|x,…”
Foundations of Linear and Generalized Linear Models (2015, background): “(1991), and Carey et al. (1993). For nominal and ordinal responses, see Touloumis et al. (2013). 9.10 Missing data: For ways of dealing with missing data in GLMs and GLMMs, see Diggle et al. (2002, Chapter 13), Hedeker and Gibbons (2006, Chapter 14), Ibrahim et al. (2005), Little and Rubin (2002), a”
Robust Estimation for Zero‐Inflated Poisson Regression (2009, methodology): “The IF is a useful and popular tool for quantifying the degree of robustness of a statistic by measuring the potential effect of an additional observation.”
Robust Analysis of Generalized Linear Mixed Models (2004, methodology): “The tuning constant c of Huber’s psi function is chosen to be 1.2 as suggested in Cantoni and Ronchetti (2001).”