Detection and Estimation of Local Signals

04/17/2020
by   Xiao Fang, et al.
0

We study the maximum score statistic to detect and estimate local signals in the form of change-points in the level, slope, or other property of a sequence of observations, and to segment the sequence when there appear to be multiple changes. We find that when observations are serially dependent, the change-points can lead to upwardly biased estimates of autocorrelations, resulting in a sometimes serious loss of power. Examples involving temperature variations, the level of atmospheric greenhouse gases, suicide rates and daily incidence of COVID-19 illustrate the general theory.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/14/2020

Cramer-von Mises tests for Change Points

We study two nonparametric tests of the hypothesis that a sequence of in...
research
11/18/2020

Robust, multiple change-point detection for covariance matrices using data depth

In this paper, two robust, nonparametric methods for multiple change-poi...
research
03/03/2020

Detecting multiple change points: a PULSE criterion

The research described herewith investigates detecting change points of ...
research
09/05/2018

A change-point problem and inference for segment signals

We address the problem of detection and estimation of one or two change-...
research
08/05/2018

α-Ball divergence and its applications to change-point problems for Banach-valued sequences

In this paper, we extend a measure of divergence between two distributio...
research
08/08/2023

Multiple Testing of Local Extrema for Detection of Structural Breaks in Piecewise Linear Models

In this paper, we propose a new generic method for detecting the number ...
research
04/20/2023

Avoiding methane emission rate underestimates when using the divergence method

Methane is a powerful greenhouse gas, and a primary target for mitigatin...

Please sign up or login with your details

Forgot password? Click here to reset