The theory and practice of statistical methods in situations where the available data are
functions (instead of real numbers or vectors) is often referred to as Functional Data Analysis …

We propose a new method to visualize and detect shape outliers in samples of curves. In
functional data analysis, we observe curves defined over a given real interval and shape …

In this paper we review the literature on ROC curves in the presence of covariates. We
discuss the different approaches that have been proposed in the literature to define, model …

We present a semiparametric statistical model for the probabilistic index which can be
defined as P (Y⩽ Y*), where Y and Y* are independent random response variables …

Proposals given in the field of ROC curves focusing on their robust aspects and
contributions are considered. The motivation is the extended belief that ROC curves are …

Supplement to “Functional covariate-adjusted partial area under the specificity-ROC curve
with an application to metabolic syndrome diagnosis”. Technical details and supplementary …

We develop a functional proportional hazards mixture cure model with scalar and functional
covariates measured at the baseline. The mixture cure model, useful in studying populations …

Comparing the accuracy and the behaviour of different diagnostic procedures is one of the
main objectives of the Receiver Operating Characteristic (ROC) curve analysis. Along with …

This is a two faces paper. Firstly, it investigates diagnostic tests in situations when the
observed variables are functional, that is, diagnostic tests that use functional variables as …

Combination of multiple biomarkers to improve diagnostic accuracy is meaningful for
practitioners and clinicians, and are attractive to lots of researchers. Nowadays, with …