Testing semiparametric model-equivalence hypotheses based on the characteristic function

10/30/2021
by   Feifei Chen, et al.
0

We propose three test criteria each of which is appropriate for testing, respectively, the equivalence hypotheses of symmetry, of homogeneity, and of independence, with multivariate data. All quantities have the common feature of involving weighted–type distances between characteristic functions and are convenient from the computational point of view if the weight function is properly chosen. The asymptotic behavior of the tests under the null hypothesis is investigated, and numerical studies are conducted in order to examine the performance of the criteria in finite samples.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/19/2020

Tests for circular symmetry of complex-valued random vectors

We propose tests for the null hypothesis that the law of a complex-value...
research
03/08/2023

Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function

We employ a general Monte Carlo method to test composite hypotheses of g...
research
04/03/2020

Equivalence testing for standardized effect sizes in linear regression

In this paper, we introduce equivalence testing procedures for standardi...
research
02/09/2018

Automatic Passenger Counting: Introducing the t-Test Induced Equivalence Test

Automatic passenger counting in public transport has been emerging rapid...
research
01/09/2019

Tests for validity of the semiparametric heteroskedastic transformation model

There exist a number of tests for assessing the nonparametric heterosced...
research
02/22/2021

Large-scale simultaneous inference under dependence

Simultaneous, post-hoc inference is desirable in large-scale hypotheses ...
research
05/05/2021

A nonparametric test of independence based on L_1-error

We propose a test of mutual independence between random vectors with arb...

Please sign up or login with your details

Forgot password? Click here to reset