Testing for linearity in boundary regression models with application to maximal life expectancies

09/30/2020
by   Jürgen Kampf, et al.
0

We consider a regression model with errors that are a.s. negative. Thus the regression function is not the expected value of the observations but the right endpoint of their support. We develop two goodness-of-fit tests for the hypotheses that the regression function is an affine function, study the asymptotic distributions of the test statistics in order to approximately fix the sizes of the tests, derive their finite-sample properties based on simulations and apply them to life expectancy data.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

11/20/2017

A new class of tests for multinormality with i.i.d. and Garch data based on the empirical moment generating function

We generalize a recent class of tests for univariate normality that are ...
06/11/2021

A new goodness of fit test for uniform distribution with censored observations

Using fixed point characterization, we develop a new goodness of fit tes...
04/05/2019

Aggregated kernel based tests for signal detection in a regression model

Considering a regression model, we address the question of testing the n...
10/06/2019

A New Graphical Device and Related Tests for the Shape of Non-parametric Regression Function

We consider a non-parametric regression model y = m(x) + ϵ and propose a...
12/27/2018

How to avoid the zero-power trap in testing for correlation

In testing for correlation of the errors in regression models the power ...
12/19/2017

The null hypothesis of common jumps in case of irregular and asynchronous observations

This paper proposes novel tests for the absence of jumps in a univariate...
11/08/2019

A Binary Regression Adaptive Goodness-of-fit Test (BAGofT)

The Pearson's χ^2 test and residual deviance test are two classical good...
This week in AI

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