Hansen, B.E. (2014) Nonparametric Sieve Regression: Least Squares, Averaging Least Squares, and Cross-Validation. In: Racine, J., Su, L.J. and Ullah, A., Eds., Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics, Oxford University Press, Oxford, 215-248.

has been cited by the following article:

• TITLE: Frequentist Model Averaging and Applications to Bernoulli Trials

  AUTHORS: Georges Nguefack-Tsague, Walter Zucchini, Siméon Fotso

  KEYWORDS: Model Selection, Post-Model Selection Estimator, Frequentist Model Averaging, Bernoulli Trials

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.3, June 28, 2016

  ABSTRACT: In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually referred to as post-model selection inference. The shortcomings of such practice are widely recognized, finding a general solution is extremely challenging. We propose a model averaging alternative consisting on taking into account model selection probability and the like-lihood in assigning the weights. The approach is applied to Bernoulli trials and outperforms Akaike weights model averaging and post-model selection estimators.