{"doi":"10.5705/ss.202022.0214","title":"Semiparametric Estimation of Non-ignorable Missingness With Refreshment Sample","abstract":"Missing data commonly arises in longitudinal data analysis and imposes methodological challenges in providing unbiased estimation and statistical inference due to informative missingness.It is crucial to correctly identify and appropriately incorporate the missing mechanism into estimation and inference procedures.Traditional methods, such as the complete-case analysis and imputation methods, are designed to deal with missing data under unverifiable assumptions of MCAR and MAR.We focus on identifying and estimating missing parameters under the non-ignorable missing assumption using refreshment samples from two-wave panel data.Specifically, we propose a full-likelihood approach when a parametric model is specified for the joint distribution of two-wave data.If the specification of the joint distribution is unavailable, a semiparametric method is proposed to estimate the attrition parameters with marginal density estimates obtained using an additional refreshment sample.We derive asymptotic properties of the semiparametric estimators and illustrate the numerical performances with simulations.Inference based on bootstrapping is proposed and assessed through simulations.A real-data application is provided based on the Netherlands Mobility Panel study.","journal":"Statistica Sinica","year":2023,"id":406514,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9527,"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":1101425,"name":"Jing Wang","orcid":"0000-0002-5960-0305","position":1,"is_corresponding":false},{"id":426500,"name":"Lan Xue","orcid":"0000-0002-7764-8564","position":2,"is_corresponding":false},{"id":337731,"name":"Annie Qu","orcid":"0000-0002-8396-7828","position":3,"is_corresponding":false},{"id":1185448,"name":"Jianfei Zheng","orcid":"0000-0001-8807-401X","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":null,"created_at":"2026-07-19T01:21:03.432924Z","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":[]}