Markov-switching decision trees
Adam T, Ötting M, Michels R (2024)
AStA Advances in Statistical Analysis 108(2): 461–476.
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| Veröffentlicht | Englisch
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**Abstract**
Decision trees constitute a simple yet powerful and interpretable machine learning tool. While tree-based methods are designed only for cross-sectional data, we propose an approach that combines decision trees with time series modeling and thereby bridges the gap between machine learning and statistics. In particular, we combine decision trees with hidden Markov models where, for any time point, an underlying (hidden) Markov chain selects the tree that generates the corresponding observation. We propose an estimation approach that is based on the expectation-maximisation algorithm and assess its feasibility in simulation experiments. In our real-data application, we use eight seasons of National Football League (NFL) data to predict play calls conditional on covariates, such as the current quarter and the score, where the model’s states can be linked to the teams’ strategies. R code that implements the proposed method is available on GitHub.
Decision trees constitute a simple yet powerful and interpretable machine learning tool. While tree-based methods are designed only for cross-sectional data, we propose an approach that combines decision trees with time series modeling and thereby bridges the gap between machine learning and statistics. In particular, we combine decision trees with hidden Markov models where, for any time point, an underlying (hidden) Markov chain selects the tree that generates the corresponding observation. We propose an estimation approach that is based on the expectation-maximisation algorithm and assess its feasibility in simulation experiments. In our real-data application, we use eight seasons of National Football League (NFL) data to predict play calls conditional on covariates, such as the current quarter and the score, where the model’s states can be linked to the teams’ strategies. R code that implements the proposed method is available on GitHub.
Stichworte
Decision trees;
EM algorithm;
Hidden Markov models;
Time series modeling
Erscheinungsjahr
2024
Zeitschriftentitel
AStA Advances in Statistical Analysis
Band
108
Ausgabe
2
Seite(n)
461–476
Urheberrecht / Lizenzen
ISSN
1863-8171
eISSN
1863-818X
Page URI
https://pub.uni-bielefeld.de/record/2990082
Zitieren
Adam T, Ötting M, Michels R. Markov-switching decision trees. AStA Advances in Statistical Analysis. 2024;108(2):461–476.
Adam, T., Ötting, M., & Michels, R. (2024). Markov-switching decision trees. AStA Advances in Statistical Analysis, 108(2), 461–476. https://doi.org/10.1007/s10182-024-00501-6
Adam, Timo, Ötting, Marius, and Michels, Rouven. 2024. “Markov-switching decision trees”. AStA Advances in Statistical Analysis 108 (2): 461–476.
Adam, T., Ötting, M., and Michels, R. (2024). Markov-switching decision trees. AStA Advances in Statistical Analysis 108, 461–476.
Adam, T., Ötting, M., & Michels, R., 2024. Markov-switching decision trees. AStA Advances in Statistical Analysis, 108(2), p 461–476.
T. Adam, M. Ötting, and R. Michels, “Markov-switching decision trees”, AStA Advances in Statistical Analysis, vol. 108, 2024, pp. 461–476.
Adam, T., Ötting, M., Michels, R.: Markov-switching decision trees. AStA Advances in Statistical Analysis. 108, 461–476 (2024).
Adam, Timo, Ötting, Marius, and Michels, Rouven. “Markov-switching decision trees”. AStA Advances in Statistical Analysis 108.2 (2024): 461–476.
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Material in PUB:
Dissertation, die diesen PUB Eintrag enthält
Statistical Inference for Stochastic Process Models in Sports Analytics
Michels R (2024)
Bielefeld: Universität Bielefeld.
Michels R (2024)
Bielefeld: Universität Bielefeld.
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