Semiparametric isotonic regression modelling and estimation for group testing data

التفاصيل البيبلوغرافية
العنوان: Semiparametric isotonic regression modelling and estimation for group testing data
المؤلفون: Jin Piao, Jing Qin, Ao Yuan, Jing Ning
المصدر: Can J Stat
بيانات النشر: Wiley, 2020.
سنة النشر: 2020
مصطلحات موضوعية: Statistics and Probability, Statistics, Expectation–maximization algorithm, Covariate, Isotonic regression, Conditional probability, Estimator, Sample (statistics), Statistics, Probability and Uncertainty, Article, Outcome (probability), Group testing, Mathematics
الوصف: In the group testing procedure, several individual samples are grouped and the pooled samples, instead of each individual sample, are tested for outcome status (e.g., infectious disease status). Although this cost-effectiveness strategy in data collection is both labor and time efficient, it poses statistical challenges to derive statistically and computationally efficient estimators under semiparametric models. We consider semiparametric isotonic regression models for the simultaneous estimation of the conditional probability curve and covariate effects, in which a parametric form for combining the covariate information is assumed and the monotonic link function is left unspecified. We develop an expectation-maximization algorithm to overcome the computational challenge and embed the pool-adjacent violators algorithm in the M-step to facilitate the computation. We establish the large sample behavior of the proposed estimators and examine their finite sample performance in simulation studies. We apply the proposed method to data from the National Health and Nutrition Examination Survey for illustration.
تدمد: 1708-945X
0319-5724
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd17cacf9d3a7cc504cc0ebaf1642703Test
https://doi.org/10.1002/cjs.11581Test
حقوق: OPEN
رقم الانضمام: edsair.doi.dedup.....cd17cacf9d3a7cc504cc0ebaf1642703
قاعدة البيانات: OpenAIRE