Post-selection inference in regression models for group testing data.
Biometrics
; 80(3)2024 Jul 01.
Article
em En
| MEDLINE
| ID: mdl-39282732
ABSTRACT
We develop a methodology for valid inference after variable selection in logistic regression when the responses are partially observed, that is, when one observes a set of error-prone testing outcomes instead of the true values of the responses. Aiming at selecting important covariates while accounting for missing information in the response data, we apply the expectation-maximization algorithm to compute maximum likelihood estimators subject to LASSO penalization. Subsequent to variable selection, we make inferences on the selected covariate effects by extending post-selection inference methodology based on the polyhedral lemma. Empirical evidence from our extensive simulation study suggests that our post-selection inference results are more reliable than those from naive inference methods that use the same data to perform variable selection and inference without adjusting for variable selection.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Simulação por Computador
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos