Inference for ROC Curves Based on Estimated Predictive Indices

12/03/2021
by   Yu-Chin Hsu, et al.
0

We provide a comprehensive theory of conducting in-sample statistical inference about receiver operating characteristic (ROC) curves that are based on predicted values from a first stage model with estimated parameters (such as a logit regression). The term "in-sample" refers to the practice of using the same data for model estimation (training) and subsequent evaluation, i.e., the construction of the ROC curve. We show that in this case the first stage estimation error has a generally non-negligible impact on the asymptotic distribution of the ROC curve and develop the appropriate pointwise and functional limit theory. We propose methods for simulating the distribution of the limit process and show how to use the results in practice in comparing ROC curves.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

09/13/2018

Receiver Operating Characteristic (ROC) Curves

Receiver operating characteristic (ROC) curves are used ubiquitously to ...
03/09/2020

Algorithm to enumerate superspecial Howe curves of genus 4

A Howe curve is a curve of genus 4 obtained as the fiber product over 𝐏^...
11/04/2020

Joint Curve Registration and Classification with Two-level Functional Models

Many classification techniques when the data are curves or functions hav...
02/29/2020

Model-based ROC (mROC) curve: examining the effect of case-mix and model calibration on the ROC plot

The performance of a risk prediction model is often characterized in ter...
05/10/2020

Statistical inference for the EU portfolio in high dimensions

In this paper, using the shrinkage-based approach for portfolio weights ...
07/17/2018

Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines

Many problems that appear in biomedical decision making, such as diagnos...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.