Browse research papers in Statistics and Probability - Mathematics Discover 4 academic papers with AI-powered analysis

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Statistics and Probability

Mathematical frameworks for data analysis and uncertainty, including statistical theory, probability theory, stochastic processes, statistical inference, and applications to data science

4 papers in this specialization

Papers

A FRAMEWORK FOR THINKING ABOUT INFORMAL STATISTICAL INFERENCE

The study proposes a framework for informal statistical inference with three key principles: generalizations beyond the data, data as evidence, and probabilistic language. Classroom episodes illustrate how teachers can foster inferential reasoning by emphasizing purposeful investigations, connecting conclusions to evidence, and promoting probabilistic language to express uncertainty.

The Exponentiated Generalized Class of Distributions

The paper proposes the "exponentiated generalized" (EG) class of distributions, a new method of adding two shape parameters to existing continuous distributions using a double Lehmann alternative construction. This extends the flexibility of distributions, particularly in the tails, enabling improved modeling in various fields. The study explores mathematical properties, including moments, generating functions, and order statistics, and demonstrates applications to real datasets from diverse areas like agriculture and material science, finding superior fits compared to existing models.

Elastic Net Regularization Paths for All Generalized Linear Models

This paper details the implementation of elastic net regularization for all generalized linear models (GLMs), Cox models with extended data types, and a simplified relaxed lasso within the glmnet R package. It also covers utility functions for evaluating fitted model performance.

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

This study argues that R-squared is a more informative and robust metric for evaluating regression models compared to SMAPE, MAE, MAPE, MSE, and RMSE. Through several synthetic use cases and analysis of two real medical datasets, the authors demonstrate that R-squared provides a more accurate assessment of model performance, particularly when dealing with skewed data or outliers. They propose using R-squared as the standard metric for regression analysis evaluation.

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Chicco-2021-PeerJ%20Comput%20Sci-VoR.pdf

Jul 14, 11:11 AM