arXiv subjects

Rodrigo F. L. Lassance

Publications and source records attributed to Rodrigo F. L. Lassance.

4 recordsLinked to original sources

arXiv2026

Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection

Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cope with before changing its prediction. However, its applicability is more limited than some of its alternatives, since it requires the use of generative models and restricts the analyses either to specific model architectures or discrete features. In this work, we propose a new robustness metric applicable to any probabilistic discriminative classifier and any type of features. We demonstrate that this new metric is capable of distinguishing between reliable and unreliable predictions, and use this observation to develop new strategies for dynamic classifier selection.

cs.LG↗

arXiv2025

Nonparametric FBST for Validating Linear Models

The Full Bayesian Significance Test (FBST) possesses many desirable aspects, such as dismissing the need for hypotheses to have positive prior probability and providing a measure of evidence against $H_0$. Still, few attempts have been made to bring the FBST to nonparametric settings, with the main drawback being the need to obtain the highest posterior density (HPD) in a function space. In this work, we use a Gaussian processes prior to derive the FBST for hypotheses of the type $$ H_0: g(\boldsymbol{x}) = \boldsymbol{b}(\boldsymbol{x})\boldsymbolβ, \quad \forall \boldsymbol{x} \in \mathcal{X}, \quad \boldsymbolβ \in \mathbb{R}^k, $$ where $g(\cdot)$ is the regression function, $\boldsymbol{b}(\cdot)$ is a vector of linearly independent linear functions -- such as $\boldsymbol{b}(\boldsymbol{x}) = \boldsymbol{x}'$ -- and $\mathcal{X}$ is the covariates' domain. We also make use of pragmatic hypotheses to verify if the data might be compatible with a linear model when factors such as measurement errors or utility judgments are accounted for. This contribution extends the theory of the FBST, allowing its application in nonparametric settings and providing a procedure that easily tests if linear models are adequate for the data and that can automatically perform variable selection.

stat.ME↗

arXiv2025

REACT to NHST: Sensible conclusions to meaningful hypotheses

While Null Hypothesis Significance Testing (NHST) remains a widely used statistical tool, it suffers from several shortcomings in its common usage, such as conflating statistical and practical significance, the formulation of inappropriate null hypotheses, and the inability to distinguish between accepting the null hypothesis and failing to reject it. Recent efforts have focused on developing alternatives that address these issues. Despite these efforts, conventional NHST remains dominant in scientific research due to its procedural simplicity and mistakenly presumed ease of interpretation. Our work presents an intuitive alternative to conventional NHST designed to bridge the gap between the expectations of researchers and the actual outcomes of hypothesis tests: $\texttt{REACT}$. $\texttt{REACT}$ not only tackles shortcomings of conventional NHST but also offers additional advantages over existing alternatives. For instance, $\texttt{REACT}$ applies to multiparametric hypotheses and does not require stringent significance-level corrections when conducting multiple tests. We illustrate the practical utility of $\texttt{REACT}$ through real-world data examples.

stat.ME↗

arXiv2024

PROTEST: Nonparametric Testing of Hypotheses Enhanced by Experts' Utility Judgements

Instead of testing solely a precise hypothesis, it is often useful to enlarge it with alternatives that are deemed to differ from it negligibly. For instance, in a bioequivalence study one might consider the hypothesis that the concentration of an ingredient is exactly the same in two drugs. In such a context, it might be more relevant to test the enlarged hypothesis that the difference in concentration between the drugs is of no practical significance. While this concept is not alien to Bayesian statistics, applications remain confined to parametric settings and strategies on how to effectively harness experts' intuitions are often scarce or nonexistent. To resolve both issues, we introduce PROTEST, an accessible nonparametric testing framework that seamlessly integrates with Markov Chain Monte Carlo (MCMC) methods. We develop expanded versions of the model adherence, goodness-of-fit, quantile and two-sample tests. To demonstrate how PROTEST operates, we make use of examples, simulated studies - such as testing link functions in a binary regression setting, as well as a comparison between the performance of PROTEST and the PTtest (Holmes et al., 2015) - and an application with data on neuron spikes. Furthermore, we address the crucial issue of selecting the threshold - which controls how much a hypothesis is to be expanded - even when intuitions are limited or challenging to quantify.

stat.ME↗