Frequentist Inference

Frequentist inference is a statistical approach focused on estimating parameters and testing hypotheses based on the observed data's frequency distribution, aiming to make objective and reproducible conclusions. Current research emphasizes developing robust frequentist methods for handling limited data, incorporating prior knowledge effectively (e.g., through PAC-Bayesian approaches and sequential updates), and improving the efficiency of inference in high-dimensional settings (e.g., using model averaging techniques for SVMs). These advancements are impacting diverse fields, from causal discovery and image analysis to concept-based learning, by providing reliable and computationally efficient tools for data analysis and decision-making in scenarios with complex data structures and limited sample sizes.

Papers

June 28, 2024

FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules
Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko
Concept Bottleneck Model Concept Learning Probabilistic Approach Annotated Image Frequentist Inference

May 23, 2024

Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif, Yevgeny Seldin
PAC Bayesian Bayesian Learning Data Driven Prior Information Loss Frequentist Inference

June 16, 2022

Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
Causal Discovery Limited Data Causal Discovery Method Cause Effect Empirical Bayes Constraint Based Causal Discovery Frequentist Inference

May 22, 2022

Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Jukka Corander, Ulpu Remes, Ida Holopainen, Timo Koski
Categorical Data Jensen Shannon Divergence Likelihood Free Inference Nonparametric Modeling Frequentist Inference

December 24, 2021

Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces
Chaoxia Yuan, Chao Ying, Zhou Yu, Fang Fang
Support Vector Machine Model Selection Model Averaging Model Space Frequentist Inference

December 2, 2021

Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects
Jesper Muren, Vilhelm Niklasson, Dmitry Otryakhin, Maxim Romashin
Anomaly Detection Data Detection Extension Study Earth Observation Imagery Deforestation Detection Non Parametric Test Frequentist Inference

Frequentist Inference

Papers

FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules

Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss

Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data

Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output

Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces

Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects