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• W3165701150 abstract "We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized numerically. The feasibility of our approach is assessed in simulation experiments. As a case study, we examine the ‘hot hand’ effect, whose existence is highly debated in different fields, such as psychology and economics. In the present work, we investigate a potential ‘hot shoe’ effect for the performance of penalty takers in (association) football, where the (latent) states of the HMM serve for the underlying form of a player." @default.
• W3165701150 created "2021-06-07" @default.
• W3165701150 creator A5088236008 @default.
• W3165701150 creator A5089224585 @default.
• W3165701150 date "2021-05-19" @default.
• W3165701150 modified "2023-09-25" @default.
• W3165701150 title "A regularized hidden Markov model for analyzing the ‘hot shoe’ in football" @default.
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• W3165701150 doi "https://doi.org/10.1177/1471082x211008014" @default.
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