Monte Carlo algorithm for trajectory optimization based on Markovian readings

Dias, Ronaldo; Garcia, Nancy L.; Zambom, Adriano Zanin

doi:10.1007/s10589-010-9337-3

Cited by 5 publications

(2 citation statements)

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“…For more detailed information regarding the strong uniform convergence, one can refer to references such as [26] or [27]. These sources provide further insights and explanations on the topic.…”

Section: Discussionmentioning

confidence: 99%

Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

Chen,

Deng

2023

OJS

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It is common for datasets to contain both categorical and continuous variables. However, many feature screening methods designed for high-dimensional classification assume that the variables are continuous. This limits the applicability of existing methods in handling this complex scenario. To address this issue, we propose a model-free feature screening approach for ultra-highdimensional multi-classification that can handle both categorical and continuous variables. Our proposed feature screening method utilizes the Maximal Information Coefficient to assess the predictive power of the variables. By satisfying certain regularity conditions, we have proven that our screening procedure possesses the sure screening property and ranking consistency properties. To validate the effectiveness of our approach, we conduct simulation studies and provide real data analysis examples to demonstrate its performance in finite samples. In summary, our proposed method offers a solution for effectively screening features in ultra-high-dimensional datasets with a mixture of categorical and continuous covariates.

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Section: Discussionmentioning

confidence: 99%

Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

Chen,

Deng

2023

OJS

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“…(the asymptotic property of nonparametric density estimators). Suppose that f ′′ (x) exists and h � cn − (1/5) ; then More details about the strong uniform convergence can be found, for example, in [26] or [27].…”

Section: Appendixmentioning

confidence: 99%

Feature Screening via Mutual Information Learning Based on Nonparametric Density Estimation

Zhou

Wang

Huang

2022

Journal of Mathematics

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With the advent of the era of big data, feature selection in high- or ultra-high-dimensional data is increasingly important in statistics and machine learning fields. In this paper, we propose a marginal utility measure screening method MI-SIS based on mutual information. The proposed marginal utility measure has several appealing features compared with the existing independence screening methods. Firstly, the proposed procedure is model-free without specifying any relationship between the predictors and the response and is valid under a wide range of model settings including parametric and nonparametric models. Secondly, it is suitable for various combinations of the continuous and categorical of predictors and response in our new method. Finally, the new procedure has a good performance in discovering a weak signal in the finite sample and its computation is simple and easy to implement. We establish the sure screening property for the proposed procedure with mild conditions. Simulation experiments and real data applications are presented to illustrate the finite sample performance of the proposed procedures.

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