Empirical and Kernel Estimation of the ROC Curve

Baszczyńska, Aleksandra

doi:10.18778/0208-6018.311.06

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…In the context of ROC analysis, kernel density estimates of the ROC curve tend to give a more conservative estimate of performance than empirical estimates in terms of AUC. 26 As such, their results when evaluated under training data are less prone to over-estimating the performance of biomarkers. Additionally, kernel-based estimates tend to provide smaller mean squared error (MSE) for D * than empirical based estimates, which is demonstrated in Figure 3.…”

Section: Proposed Approaches For Combining Biomarkersmentioning

confidence: 99%

Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings

Mosier,

Bantis

2024

Stat Methods Med Res

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The performance of individual biomarkers in discriminating between two groups, typically the healthy and the diseased, may be limited. Thus, there is interest in developing statistical methodologies for biomarker combinations with the aim of improving upon the individual discriminatory performance. There is extensive literature referring to biomarker combinations under the two-class setting. However, the corresponding literature under a three-class setting is limited. In our study, we provide parametric and nonparametric methods that allow investigators to optimally combine biomarkers that seek to discriminate between three classes by minimizing the Euclidean distance from the receiver operating characteristic surface to the perfection corner. Using this Euclidean distance as the objective function allows for estimation of the optimal combination coefficients along with the optimal cutoff values for the combined score. An advantage of the proposed methods is that they can accommodate biomarker data from all three groups simultaneously, as opposed to a pairwise analysis such as the one implied by the three-class Youden index. We illustrate that the derived true classification rates exhibit narrower confidence intervals than those derived from the Youden-based approach under a parametric, flexible parametric, and nonparametric kernel-based framework. We evaluate our approaches through extensive simulations and apply them to real data sets that refer to liver cancer patients.

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Section: Proposed Approaches For Combining Biomarkersmentioning

confidence: 99%

Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings

Mosier,

Bantis

2024

Stat Methods Med Res

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“…Kernel method is widely used in estimation of some functional characteristics of random variable, such as density function, cumulative distribution function, receiver operating characteristic or regression function (cf. : [1,2,3,4,5,6]) as well as in [51] estimation of some characteristic parameters moments or positional parameters including quantiles (cf. : [7,8,9]).…”

Section: Introductionmentioning

confidence: 99%

Smoothing parameter values in automatic choice procedure and in acceptable interval in the kernel density estimation

Baszczyńska

2018

Bull

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Automatic procedure for determining the parameters of kernel method, allows the simultaneous selection of two method parameters: kernel function and smoothing parameter. This approach simplifies the procedure for parameters selection and at the same time provides a good properties of kernel estimators. The second procedure regarded in the paper is the acceptable interval of values of smoothing parameter, allowing for a much more generalized approach in choosing the smoothing parameter in the kernel estimation. The results of the smoothing parameter values comparison, where these values are set in the automatic procedure and the procedure of the acceptable interval of smoothing parameters values in the estimation of density function, are presented in the paper. Comparison of these values is made basing on the results of applying the simulation methods. Basing on simulation studies results new intervals of values of smoothing parameter are proposed and analyzed.

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Empirical and Kernel Estimation of the ROC Curve

Cited by 2 publications

References 2 publications

Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings

Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings

Smoothing parameter values in automatic choice procedure and in acceptable interval in the kernel density estimation

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