Underreporting of drug use among individuals with schizophrenia: Prevalence and predictors

Bahorik, Amber L.; Newhill, Christina E.; Queen, Courtney C.; Cornelius, Jack R.; Eack, Shaun M.

doi:10.1016/j.drugalcdep.2014.02.046

Cited by 1 publication

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“…Many examples include disease data (diagnosed patients with a specific disease detected (S = 1), and patients yet to be diagnosed who may be ill or not (S = 0)), web pages preferences of specific users (pages bookmarked as 'of interest' and pages not yet viewed) and ecological examples when habitats are labeled if a specific species of interest lives there, and unlabeled if this species has not been spotted yet. For representative examples of such applications, see, e.g., the works of Bahorik et al (2014), Ward et al (2009), Liu et al (2003) and Yang et al (2014). Another group of important applications from a different domain is under-reporting in survey data when some respondents fail to give a truthful answer to a sensitive question.…”

Section: Introductionmentioning

confidence: 99%

Revisiting strategies for fitting logistic regression for positive and unlabeled data

Adam

Jan

2022

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Positive unlabeled (PU) learning is an important problem motivated by the occurrence of this type of partial observability in many applications. The present paper reconsiders recent advances in parametric modeling of PU data based on empirical likelihood maximization and argues that they can be significantly improved. The proposed approach is based on the fact that the likelihood for the logistic fit and an unknown labeling frequency can be expressed as the sum of a convex and a concave function, which is explicitly given. This allows methods such as the concave-convex procedure (CCCP) or its variant, the disciplined convex-concave procedure (DCCP), to be applied. We show by analyzing real data sets that, by using the DCCP to solve the optimization problem, we obtain significant improvements in the posterior probability and the label frequency estimation over the best available competitors.

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Section: Introductionmentioning

confidence: 99%

Revisiting strategies for fitting logistic regression for positive and unlabeled data

Adam

Jan

2022

View full text Add to dashboard Cite

show abstract

Underreporting of drug use among individuals with schizophrenia: Prevalence and predictors

Cited by 1 publication

References 0 publications

Revisiting strategies for fitting logistic regression for positive and unlabeled data

Revisiting strategies for fitting logistic regression for positive and unlabeled data

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