3 citations · 5 across the 6 of their papers we have counts for
7 papers
Adaptive Bag of Little Bootstraps for Nonsmooth Robust Inference: A Stability-Driven Framework with Reproducible Cholera Surveillance Illustration from the Democratic Republic of the Congo
Cosmas Kambale Musambi, Yoda Amidou, Maiga Mahafouz +3
Bootstrap inference is a cornerstone of computational statistics, but classical bootstrap and BCa intervals can be unstable for nonsmooth estimators such as empirical quantiles, sa…
No Intelligence Without Statistics: The Invisible Backbone of Artificial Intelligence
Ernest Fokoué
The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental trut…
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities
Kameron B. Kinast, Ernest Fokoué
In this paper, we present a comprehensive analysis of extreme temperature patterns using emerging statistical machine learning techniques. Our research focuses on exploring and com…
A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data
Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou +2
This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures fro…
What do Asian Religions Have in Common? An Unsupervised Text Analytics Exploration
Preeti Sah, Ernest Fokoué
The main source of various religious teachings is their sacred texts which vary from religion to religion based on different factors like the geographical location or time of the b…
Bayesian Variable Selection for Linear Regression with the - Priors
Zichen Ma, Ernest Fokoué
In this paper, we introduce a new methodology for Bayesian variable selection in linear regression that is independent of the traditional indicator method. A diagonal matrix $\math…
Prediction Error Reduction Function as a Variable Importance Score
Ernest Fokoué
This paper introduces and develops a novel variable importance score function in the context of ensemble learning and demonstrates its appeal both theoretically and empirically. Ou…