Academic literature on the topic 'Penalized log-likelihood criterion'
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Journal articles on the topic "Penalized log-likelihood criterion"
Gao, Yongfeng, Siming Lu, Yongyi Shi, Shaojie Chang, Hao Zhang, Wei Hou, Lihong Li, and Zhengrong Liang. "A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT." Sensors 23, no. 3 (January 26, 2023): 1374. http://dx.doi.org/10.3390/s23031374.
Full textTang, Jiarui, An-Min Tang, and Niansheng Tang. "Variable selection for joint models of multivariate skew-normal longitudinal and survival data." Statistical Methods in Medical Research, July 5, 2023. http://dx.doi.org/10.1177/09622802231181767.
Full textPluntz, Matthieu, Cyril Dalmasso, Pascale Tubert‐Bitter, and Ismaïl Ahmed. "A Simple Information Criterion for Variable Selection in High‐Dimensional Regression." Statistics in Medicine, December 12, 2024. https://doi.org/10.1002/sim.10275.
Full textLeonardi, Florencia, Rodrigo Carvalho, and Iara Frondana. "Structure recovery for partially observed discrete Markov random fields on graphs under not necessarily positive distributions." Scandinavian Journal of Statistics, August 2, 2023. http://dx.doi.org/10.1111/sjos.12674.
Full textDissertations / Theses on the topic "Penalized log-likelihood criterion"
Aubert, Julien. "Théorie de l'estimation pour les processus d'apprentissage." Electronic Thesis or Diss., Université Côte d'Azur, 2025. http://www.theses.fr/2025COAZ5001.
Full textThis thesis considers the problem of estimating the learning process of an individual during a task based on observed choices or actions of that individual. This question lies at the intersection of cognition, statistics, and reinforcement learning, and involves developing models that accurately capture the dynamics of learning, estimating model parameters, and selecting the best-fitting model. A key difficulty is that learning, by nature, leads to non-independent and non-stationary data, as the individual selects its actions depending on the outcome of its previous choices.Existing statistical theories and methods are well-established for independent and stationary data, but their application to a learning framework introduces significant challenges. This thesis seeks to bridge the gap between empirical methods and theoretical guarantees in computational modeling. I first explore the properties of maximum likelihood estimation on a model of learning based on a bandit problem. I then present general theoretical results on penalized log-likelihood model selection for non-stationary and dependent data, for which I develop a new concentration inequality for the suprema of renormalized processes. I also introduce a hold-out procedure and theoretical guarantees for it in a learning framework. These theoretical results are supported with applications on synthetic data and on real cognitive experiments in psychology and ethology
Book chapters on the topic "Penalized log-likelihood criterion"
Dawid, A. P. "Prequential Analysis, Stochastic Complexity and Bayesian Inference." In Bayesian Statistics 4, 109–26. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198522669.003.0007.
Full textAnderson, Raymond A. "Stats & Maths & Unicorns." In Credit Intelligence & Modelling, 405–34. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0011.
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