Tootfinder

One-shot variable-ratio matching with fine balance
Qian Meng, Chen Zhe, Bo Zhang
https://arxiv.org/abs/2510.04383 https://arxiv.org/pdf/2510.04383

One-shot variable-ratio matching with fine balance
Variable-ratio matching is a flexible alternative to conventional $1$-to-$k$ matching for designing observational studies that emulate a target randomized controlled trial (RCT). To achieve fine balance -- that is, matching treated and control groups to have the same marginal distribution on selected covariates -- conventional approaches typically partition the data into strata based on estimated entire numbers and then perform a series of $1$-to-$k$ matches within each stratum, with $k$ determ…

A strategy to avoid particle depletion in recursive Bayesian inference
Henry R. Scharf
https://arxiv.org/abs/2508.01572 https://arxiv.org/pdf/2508.01572

A strategy to avoid particle depletion in recursive Bayesian inference
Recursive Bayesian inference, in which posterior beliefs are updated in light of accumulating data, is a tool for implementing Bayesian models in applications with streaming and/or very large data sets. As the posterior of one iteration becomes the prior for the next, beliefs are updated sequentially instead of all-at-once. Thus, recursive inference is relevant for both streaming data and settings where data too numerous to be analyzed together can be partitioned into manageable pieces. In prac…

Scalable Estimation of Crossed Random Effects Models via Multi-way Grouping
Shota Takeishi, Shonosuke Sugasawa
https://arxiv.org/abs/2507.15593 https://

Scalable Estimation of Crossed Random Effects Models via Multi-way Grouping
Cross-classified data frequently arise in scientific fields such as education, healthcare, and social sciences. A common modeling strategy is to introduce crossed random effects within a regression framework. However, this approach often encounters serious computational bottlenecks, particularly for non-Gaussian outcomes. In this paper, we propose a scalable and flexible method that approximates the distribution of each random effect by a discrete distribution, effectively partitioning the rand…

A tree-based Polynomial Chaos expansion for surrogate modeling and sensitivity analysis of complex numerical models
Faten Ben Said (CERMICS, EDF R\&D LNHE), Aur\'elien Alfonsi (CERMICS, MATHRISK), Anne Dutfoy (EDF R\&D PERICLES), C\'edric Goeury (EDF R\&D LNHE, LHSV), Magali Jodeau (EDF R\&D LNHE, LHSV), Julien Reygner (CERMICS, RT-UQ), Fabrice Zaoui (EDF R\&D LNHE)

A tree-based Polynomial Chaos expansion for surrogate modeling and sensitivity analysis of complex numerical models
This paper introduces Tree-based Polynomial Chaos Expansion (Tree-PCE), a novel surrogate modeling technique designed to efficiently approximate complex numerical models exhibiting nonlinearities and discontinuities. Tree-PCE combines the expressive power of Polynomial Chaos Expansion (PCE) with an adaptive partitioning strategy inspired by regression trees. By recursively dividing the input space into hyperrectangular subdomains and fitting localized PCEs, Tree-PCE constructs a piecewise polyn…

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
Sourav Chakrabarty, Anirvan Chakraborty, Shyamal K. De
https://arxiv.org/abs/2507.11158

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
We develop novel clustering algorithms for functional data when the number of clusters $K$ is unknown and also when it is prefixed. These algorithms are developed based on the Maximum Mean Discrepancy (MMD) measure between two sets of observations. The algorithms recursively use a binary splitting strategy to partition the dataset into two subgroups such that they are maximally separated in terms of an appropriate weighted MMD measure. When $K$ is unknown, the proposed clustering algorithm has …