Statistical Inference: The Minimum Distance Approach

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Robust generalised Bayesian inference for intractable likelihoods (2021, neutral): “Minimum distance estimation, which can be considered the frequentist analogue of generalised Bayesian inference, can strike an attractive balance between these competing goals (see e.g. Lindsay, 1994; Basu et al., 2019).”
Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach (2015, methodology): “[2] provide an extended description of the methods based on the DPD and other related minimum distance methods.”
Generalized Wald-type tests based on minimum density power divergence estimators (2014, background): “,[5] form the basis of our subsequent developments.”
Robust linear regression for high‐dimensional data: An overview (2020, methodology): “There are also alternative approaches to robustness available, such as methods based on a minimal distance concept, see Scott (2001) or Basu, Shioya, and Park (2011), for an overview.”
Testing statistical hypotheses based on the density power divergence (2012, background): “(b) n1/2(θ̂β − θ g) has an asymptotic multivariate normal distribution with (vector) mean zero and covariance matrix J−1KJ−1, where J = Jβ(θ g), K = Kβ(θ g), and Jβ and Kβ are as in (4) and (5) respectively; see Basu et al. (1998) and Basu et al. (2011).”