{"doi":"10.5705/ss.202022.0261","title":"On P-Value Combination of Independent and Non-sparse Signals: Asymptotic Efficiency and Fisher Ensemble","abstract":"Combining p-values to integrate multiple effects is of long-standing interest in social science and biomedical research.In this paper, we revisit a classical scenario closely related to meta-analysis with unknown heterogeneity, which combines finite and fixed number of p-values while the sample size for generating each p-value can go to infinity.Many modified Fisher's methods have been developed in the past decade for this purpose but their asymptotic properties and finite-sample numerical performance have not been developed, which will be pursued in this paper.The result concludes that Fisher and adaptive rank truncated product method have top performance and complementary advantages across different proportions of true signals.Consequently, we propose an ensemble method, namely Fisher ensemble, to combine the two top-performing Fisher-related methods using a robust harmonic mean ensemble approach.We show that Fisher ensemble achieves asymptotic Bahadur optimality and integrates strengths of the two methods in simulations.We subsequently extend Fisher ensemble to a variation with emphasized power for concordant effect size directions.A transcriptomic meta-analysis application confirms the theoretical and simulation conclusions, generates intriguing biomarker and pathway findings, and demonstrates the strengths and strategy of using the proposed Fisher ensemble methods.","journal":"Statistica Sinica","year":2023,"id":386054,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9522,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":838504,"name":"Chung Chang","orcid":null,"position":1,"is_corresponding":false},{"id":231798,"name":"George C. Tseng","orcid":"0000-0002-5447-1014","position":2,"is_corresponding":false},{"id":327890,"name":"Yusi Fang","orcid":null,"position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T01:17:56.803401Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}