Bayesian Nonparametric ROC Regression Modeling
| Autor: | Timothy Hanson, Miguel de Carvalho, Vanda Inacio de Carvalho, Alejandro Jara |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Statistics and Probability
Mean squared error Receiver operating characteristic business.industry Conditional area under the curve Applied Mathematics Pattern recognition Markov chain Monte Carlo Regression analysis Dirichlet distribution symbols.namesake Statistics Covariate symbols dependent Dirichlet process Probability distribution Artificial intelligence business related probability distributions Categorical variable Mathematics |
| Zdroj: | Bayesian Anal. 8, no. 3 (2013), 623-646 BAYESIAN ANALYSIS Artículos CONICYT CONICYT Chile instacron:CONICYT Calhau Fernandes Inacio De Carvalho, V, Jara, A, Hanson, T E & de Carvalho, M 2013, ' Bayesian Nonparametric ROC Regression Modeling ', Bayesian analysis, vol. 8, no. 3, pp. 623-646 . https://doi.org/10.1214/13-BA825 |
| Popis: | The receiver operating characteristic (ROC) curve is the most widely used measure for evaluating the discriminatory performance of a continuous biomarker. Incorporating covariates in the analysis can potentially enhance information gath- ered from the biomarker, as its discriminatory ability may depend on these. In this paper we propose a dependent Bayesian nonparametric model for conditional ROC estimation. Our model is based on dependent Dirichlet processes, where the covariate-dependent ROC curves are indirectly modeled using probability models for related probability distributions in the diseased and healthy groups. Our ap- proach allows for the entire distribution in each group to change as a function of the covariates, provides exact posterior inference up to a Monte Carlo error, and can easily accommodate multiple continuous and categorical predictors. Simula- tion results suggest that, regarding the mean squared error, our approach performs better than its competitors for small sample sizes and nonlinear scenarios. The proposed model is applied to data concerning diagnosis of diabetes. |
| Databáze: | OpenAIRE |
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