{"ID":2868977,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16337","arxiv_id":"2509.16337","title":"Learning Centre Partitions from Summaries","abstract":"Multi-centre studies increasingly rely on distributed inference, where sites share only centre-level summaries. Homogeneity of parameters across centres is often violated, motivating methods that both \\emph{test} for equality and \\emph{learn} centre groupings before estimation. We develop multivariate Cochran-type tests that operate on summary statistics and embed them in a sequential, test-driven \\emph{Clusters-of-Centres (CoC)} algorithm that merges centres (or blocks) only when equality is not rejected. We derive the asymptotic $χ^2$-mixture distributions of the test statistics and provide plug-in estimators for implementation. To improve finite-sample integration, we introduce a multi-round bootstrap CoC that re-evaluates merges across independently resampled summary sets; under mild regularity and a separation condition, we prove a \\emph{golden-partition recovery} result: as the number of rounds grows with $n$, the true partition is recovered with probability tending to one. We also give simple numerical guidelines, including a plateau-based stopping rule, to make the multi-round procedure reproducible. Simulations and a real-data analysis of U.S.\\ airline on-time performance (2007) show accurate heterogeneity detection and partitions that change little with the choice of resampling scheme.","short_abstract":"Multi-centre studies increasingly rely on distributed inference, where sites share only centre-level summaries. Homogeneity of parameters across centres is often violated, motivating methods that both \\emph{test} for equality and \\emph{learn} centre groupings before estimation. We develop multivariate Cochran-type test...","url_abs":"https://arxiv.org/abs/2509.16337","url_pdf":"https://arxiv.org/pdf/2509.16337v2","authors":"[\"Zinsou Max Debaly\",\"Jean-Francois Ethier\",\"Michael H. Neumann\",\"Félix Camirand-Lemyre\"]","published":"2025-09-19T18:25:06Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.ST\",\"stat.AP\",\"stat.ML\"]","methods":"[]","has_code":false}