Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression

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A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator (2016, methodology): “Here, the matrices J∗ (β0) and K ∗ (β0) can be obtained directly from the general results of Ghosh and Basu (2013) or from the simplified results in the context of Bernoulli logistic regression with fixed design in Ghosh and Basu (2015) and are given by J∗ (β0) = I∑ i=1 αie xTi β eλx T i β + ex T i…”
Robust estimation in generalized linear models: the density power divergence approach (2014, methodology): “Ghosh and Basu (2013a, 2013b) also observed similar trade-oﬀs for the linear regression case with ﬁxed covariates.”
Generalized Wald-type tests based on minimum density power divergence estimators (2014, methodology): “The following results, proved in Basu et al.,[5] form the basis of our subsequent developments.”
Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach (2015, background): “See Ghosh and Basu [5] for a detailed discussion of the IF and summary measures based on it.”