Abstract
In this article we describe an extension based on relevance weighting of Wald’s classical likelihood theory. The extension allows bias to be traded for precision in the likelihood setting like bias is traded for variance in nonparametric regression. All relevant sample information can thereby be used while bias is filtered out. We describe and demonstrate the use of both the nonparametric and parametric likelihoods. The latter is used to develop a method for forecasting goals in ice-hockey.
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Hu, F., Zidek, J.V. (2001). The Relevance Weighted Likelihood With Applications. In: Ahmed, S.E., Reid, N. (eds) Empirical Bayes and Likelihood Inference. Lecture Notes in Statistics, vol 148. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0141-7_13
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DOI: https://doi.org/10.1007/978-1-4613-0141-7_13
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