On the Asymptotic Distribution of the Scan Statistic for Point Clouds

10/04/2019
by   Andrew Ying, et al.
0

We derive the large-sample distribution of several variants of the scan statistic applied to a point process on an interval, which can be applied to detect the presence of an anomalous interval with any length. The main ingredients in the proof are Kolmogorov's theorem, a Poisson approximation, and recent technical results by Kabluchko et al (2014).

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/26/2017

An expectation-based space-time scan statistic for ZIP-distributed data

An expectation-based scan statistic is proposed for the prospective moni...
research
11/11/2019

On Cramér-von Mises statistic for the spectral distribution of random matrices

Let F_N and F be the empirical and limiting spectral distributions of an...
research
11/06/2020

Detecting spatial clusters on functional data: a parametric scan statistic approach

This paper proposes a parametric scan statistic for detecting clusters o...
research
08/13/2020

Calibrating the scan statistic: finite sample performance vs. asymptotics

We consider the problem of detecting an elevated mean on an interval wit...
research
02/23/2018

Detection of Sparse Mixtures: Higher Criticism and Scan Statistic

We consider the problem of detecting a sparse mixture as studied by Ings...
research
02/16/2018

Learning Patterns for Detection with Multiscale Scan Statistics

This paper addresses detecting anomalous patterns in images, time-series...
research
11/03/2021

Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data

This work introduces a novel multivariate temporal point process, the Pa...

Please sign up or login with your details

Forgot password? Click here to reset