Abstract
A proposal of Van der Vaart (1996) for an adaptive estimator of a location parameter from a family of normal scale mixtures is explored. Recent developments in convex optimization have dramatically improved the computational feasibility of the Kiefer and Wolfowitz (Ann Math Stat 27:887–906, 1956) nonparametric maximum likelihood estimator for general mixture models and yield an effective strategy for estimating the efficient score function for the location parameter in this setting. The approach is extended to regression and performance is evaluated with a small simulation experiment.
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This research was partially supported by NSF grant SES-11-53548.
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Koenker, R. (2015). Adaptive Estimation of Regression Parameters for the Gaussian Scale Mixture Model. In: Beran, J., Feng, Y., Hebbel, H. (eds) Empirical Economic and Financial Research. Advanced Studies in Theoretical and Applied Econometrics, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-03122-4_23
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DOI: https://doi.org/10.1007/978-3-319-03122-4_23
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