Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function

01/25/2022
by   Mark Holmes, et al.
0

We propose nonparametric open-end sequential testing procedures that can detect all types of changes in the contemporary distribution function of multivariate observations. Their asymptotic properties are theoretically investigated under stationarity and under alternatives to stationarity. Monte Carlo experiments reveal their good finite-sample behavior in the case of continuous univariate observations. A short data example concludes the work.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/26/2020

Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions

The aim of sequential change-point detection is to issue an alarm when i...
research
07/16/2020

Open-end nonparametric sequential change-point detection based on the retrospective CUSUM statistic

The aim of online monitoring is to issue an alarm as soon as there is si...
research
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 ...
research
08/10/2020

Nonparametric prediction with spatial data

We describe a nonparametric prediction algorithm for spatial data. The a...
research
06/20/2019

Sequential Rank Shiryaev-Roberts CUSUMs

We develop Shiryaev-Roberts type CUSUMs based on signed sequential ranks...
research
10/20/2017

Nonparametric estimation of multivariate distribution function for truncated and censored lifetime data

In this article we consider a number of models for the statistical data ...
research
04/21/2015

Nonparametric Testing for Heterogeneous Correlation

In the presence of weak overall correlation, it may be useful to investi...

Please sign up or login with your details

Forgot password? Click here to reset