Choosing a robustness tuning parameter
By Janette Warwick, M. Chris Jones. 2005
160 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ec81ec9eb51b7ab9e97a6b418e03a6f581ac5c6b
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In Statistics and Probability
Citations in the corpus
- Robust estimation in generalized linear models: the density power divergence approach (2014, methodology): “In the context of the i.i.d. data problems, some data driven choices for selecting the optimum tuning parameter in the minimum density power divergence estimation context have been proposed by Hong and Kim (2001) and Warwick and Jones (2005).”
- Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach (2015, methodology): “Warwick and Jones [14] proposed to minimize an asymptotic approximation of the summed mean-squared error (MSE) E[(θ̂α − θ∗)T(θ̂α − θ∗)] to choose the optimal α.”
- A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator (2016, methodology): “Following Warwick and Jones (2005), we need to minimize an estimate of the MSE of the MDPDE β̂λ as an function of the tuning parameter λ given by MSE(λ) = (βλ − β∗) T (βλ − β∗) + 1 n Trace ( J−1λ (βλ)Kλ(βλ)J −1 λ (βλ) ) , (7.1) where β∗ is the true value of the target parameter and βλ is the best…”
- Generalized Wald-type tests based on minimum density power divergence estimators (2014, methodology): “In this context we prefer to begin with the MDPDE corresponding to β = 0.5 as the pilot estimator, and determine the data driven choice of the tuning parameter β following the approach of Warwick and Jones.”