DeepAI AI Chat
Log In Sign Up

Comparing regression curves – an L^1-point of view

02/02/2023
by   Patrick Bastian, et al.
0

In this paper we compare two regression curves by measuring their difference by the area between the two curves, represented by their L^1-distance. We develop asymptotic confidence intervals for this measure and statistical tests to investigate the similarity/equivalence of the two curves. Bootstrap methodology specifically designed for equivalence testing is developed to obtain procedures with good finite sample properties and its consistency is rigorously proved. The finite sample properties are investigated by means of a small simulation study.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/30/2020

An automatic procedure to determine groups of nonparametric regression curves

In many situations it could be interesting to ascertain whether nonparam...
05/29/2019

On Some Resampling Procedures with the Empirical Beta Copula

The empirical beta copula is a simple but effective smoother of the empi...
11/23/2022

Shapley Curves: A Smoothing Perspective

Originating from cooperative game theory, Shapley values have become one...
10/21/2017

Functional data analysis in the Banach space of continuous functions

Functional data analysis is typically conducted within the L^2-Hilbert s...
08/12/2019

Identifying shifts between two regression curves

This article studies the problem whether two convex (concave) regression...
10/19/2019

Equivalence tests for binary efficacy-toxicity responses

Clinical trials often aim to compare a new drug with a reference treatme...
12/20/2021

Generalized Pareto Regression Trees for extreme events analysis

In this paper, we provide finite sample results to assess the consistenc...