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In this article we focus on integrative estimation of distributed heterogeneous precision matrices, a crucial task related to joint precision matrix estimation where computation-efficient algorithms and statistical optimality theories are still underdeveloped. To tackle these challenges, we introduce a novel HEterogeneity-adjusted Aggregating and Thresholding (HEAT) approach for distributed integrative estimation. HEAT is designed to be both communication- and computation-efficient, and we demonstrate its statistical optimality by establishing the convergence rates and the corresponding minimax lower bounds under various integrative losses. To enhance the optimality of HEAT, we further propose an iterative HEAT (IteHEAT) approach. By iteratively refining the higher-order errors of HEAT estimators through multi-round communications, IteHEAT achieves geometric contraction rates of convergence. Extensive simulations and real data applications validate the numerical performance of HEAT and IteHEAT methods.<\/jats:p>","DOI":"10.1214\/26-aos2620","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T12:47:18Z","timestamp":1790945238000},"page":"2209-2233","source":"Crossref","is-referenced-by-count":0,"title":["Optimal integrative estimation for distributed precision matrices with heterogeneity adjustment"],"prefix":"10.1214","volume":"54","author":[{"given":"Yinrui","family":"Sun","sequence":"first","affiliation":[{"name":"Faculty of Business for Science & Technology, School of Management, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"55","doi-asserted-by":"publisher","unstructured":"Lounici, K.<\/small>, Pontil, M.<\/small>, van de Geer, S.<\/small> and Tsybakov, A. 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