Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1912.11369

AVaN Pack: An Analytical/Numerical Solution for Variance-Based Sensitivity Analysis

Eduardo Vasconcelos·Adriano Souza·Kelvin Dias
View original source ↗Read source document ↗Export metadata ↓

Abstract

Sensitivity analysis is an important concept to analyze the influences of parameters in a system, an equation or a collection of data. The methods used for sensitivity analysis are divided into deterministic and statistical techniques. Generally, deterministic techniques analyze fixed points of a model whilst stochastic techniques analyze a range of values. Deterministic methods fail in analyze the entire range of input values and stochastic methods generate outcomes with random errors. In this manuscript, we are interested in stochastic methods, mainly in variance-based techniques such as Variance and Sobol indices, since this class of techniques is largely used on literature. The objective of this manuscript is to present an analytical solution for variance based sensitive analysis. As a result of this research, two small programs were developed in Javascript named as AVaN Pack (Analysis of Variance through Numerical solution). These programs allow users to find the contribution of each individual parameter in any function by means of a mathematical solution, instead of sampling-based ones.

Explore related subjects

stat.OTcs.MS

Keep this discovery

BibTeXRIS

Eduardo Vasconcelos, Adriano Souza, Kelvin Dias. 2019-12-22. AVaN Pack: An Analytical/Numerical Solution for Variance-Based Sensitivity Analysis. https://arxiv.org/abs/1912.11369

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Design and Implementation of a Virtual Statistical Computing Lab to Teach R Coding to Introductory Statistics Students

Motivated by national calls for computationally enriched, data-centric instruction across the statistics curriculum, this study investigates the design, implementation, and impact of a Virtual Statistical Computing Lab (VSCL) integrated into an introductory statistics course at a medium-sized minority-serving university in the USA. The redesigned course embedded R-based coding through two virtual lab formats: Design I (a static Posit Cloud environment) and Design II (an interactive learnr-based interface). Using a quasi-experimental design across three instructional formats, traditional (no lab), Design I, and Design II, we evaluated students' conceptual learning gains, levels of data science (DS) readiness, and DS aspirations. The results indicated significant learning gains across all groups, with the highest gains observed in Design II. Students in both VSCL formats achieved greater gains in DS readiness than the traditional group, with Design II again yielding the largest gains across the demographic subgroups. Conversely, DS aspirations remained low or declined, suggesting a gap between skill acquisition and long-term interest. These findings highlight the value of structured, interactive computing environments in supporting statistical reasoning and building confidence in modern data tools. They also point to the need for intentional curricular bridges and career mentoring to help students translate early computing exposure into sustained academic and professional pathways in statistics and data science.

stat.OT↗
arXiv2026

Statistical Theory in the Age of Machine-Assisted Mathematics: Rethinking How Theory Is Made and Taught

The computational revolution is advancing at an unprecedented pace. The combination of proof-assistant technologies and generative AI tools has recently enabled the solution of complex problems in pure mathematics at a scale that seemed unattainable only a few years ago. However, these technologies have not yet become standard tools in the development of statistical theory. In this paper, we do not present new theoretical results. Instead, we discuss five case studies involving classical problems in statistics and describe how they can be analyzed using a machine proof-checking. Our goal is not to propose a definitive workflow, but to stimulate reflection on how these technologies may transform theoretical research and advanced statistical education. We focus on two main aspects. First, statistical theory often compresses substantial mathematical content into expressions such as "under the usual regularity conditions". Formalization in a machine-verifiable language forces each assumption to be explicit, reveal hidden dependencies, and provide a deeper understanding of the formalized objects. Second, we argue that the statistical community could benefit from a collaborative effort to build repositories of formalized axioms, definitions, and theorems, supporting more precise and reliable theoretical developments. Finally, we discuss the role of these tools in graduate education. Just as high-level programming languages revolutionized empirical research by enabling rapid experimentation and prototyping, machine-assisted formalization may introduce a new paradigm for the development, verification, and communication of statistical theory.

stat.OT↗
arXiv2026

Statistical Leadership of What? Statistics After AI

Statisticians have spent over a century arguing that we are more than calculators, usually by pointing to what else we know. AI is making that defense harder, since the list of what only statisticians can do grows shorter with each model release. AI makes claims cheap to generate and may eventually make the statistics behind them cheap too. However, a model cannot be answerable in the way that statistical practice requires. Statistical leadership then becomes a question of which claims we are there to answer for, including the ones we answer for in advance by building judgment into systems.

stat.OT↗