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Goodness-of-fit tests for a logistic regression model with missing covariates.
Lee, Shen-Ming; Tran, Phuoc-Loc; Li, Chin-Shang.
Afiliação
  • Lee SM; Department of Statistics, 34902Feng Chia University, Taiwan, ROC.
  • Tran PL; Department of Statistics, 34902Feng Chia University, Taiwan, ROC.
  • Li CS; Department of Mathematics, College of Natural Science, 95400Can Tho University, Viet Nam.
Stat Methods Med Res ; 31(6): 1031-1050, 2022 06.
Article em En | MEDLINE | ID: mdl-35345942
Model checking for logistic regression with covariates missing at random is considered. Based on the ideas of Copas (1989) and Osius and Rojek (1992) and studies of Homser et al. (1997), proposed are the two-type goodness-of-fit tests, Pearson chi-squared and unweighted residual sum-of-squares tests, in which their test statistics are centralized by subtracting their estimated mean to be mean-zero-form test statistics via the inverse probability weighting (IPW) and nonparametric multiple imputation (MI) methods to solve the missing value problem. The asymptotic properties of these test statistics are established under the null hypothesis and some regularity conditions. The test statistics conducted by using the IPW and MI estimators are asymptotically equivalent. Proposed are the IPW method and two bootstrap re-sampling approaches for estimation of the variances of the proposed test statistics to solve the issue of underestimating their variances by the MI method of Rubin (1987). Simulation studies are carried out to assess the finite-sample power performances of these proposed tests. Two real data examples are used to illustrate the applicability of the proposed tests.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Logísticos Tipo de estudo: Risk_factors_studies Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Logísticos Tipo de estudo: Risk_factors_studies Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2022 Tipo de documento: Article