Nonparametric methods for forecasting the Cox process

Kenneth M. Hutchison, Thomas Reed Willemain

Research output: Contribution to conferencePaper

Abstract

A Cox process is a Poisson process whose parameter is itself a stochastic process. Current methods used to estimate the underlying intensity process, such as principal component analysis, Markov chain Monte Carlo methods, and maximum likelihood estimation, are limited by memory requirements and computational times to small problems. We introduce a computationally efficient, nonparametric estimation method, REX (Reversed Exponential Smoothing) that allows for the analysis of much larger problems. We report the results of a factorial experiment to test the accuracy of these methods when the underlying intensity is generated by a higher-order autoregressive process. We assess accuracy using Kullback-Liebler Divergence[1].

Original languageEnglish (US)
Pages2590-2599
Number of pages10
StatePublished - Jan 1 2013
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: May 18 2013May 22 2013

Other

OtherIIE Annual Conference and Expo 2013
CountryPuerto Rico
CitySan Juan
Period5/18/135/22/13

Fingerprint

Maximum likelihood estimation
Random processes
Principal component analysis
Markov processes
Monte Carlo methods
Data storage equipment
Experiments

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Hutchison, K. M., & Willemain, T. R. (2013). Nonparametric methods for forecasting the Cox process. 2590-2599. Paper presented at IIE Annual Conference and Expo 2013, San Juan, Puerto Rico.
Hutchison, Kenneth M. ; Willemain, Thomas Reed. / Nonparametric methods for forecasting the Cox process. Paper presented at IIE Annual Conference and Expo 2013, San Juan, Puerto Rico.10 p.
@conference{63a4e23947e248d6ae16c16a68228189,
title = "Nonparametric methods for forecasting the Cox process",
abstract = "A Cox process is a Poisson process whose parameter is itself a stochastic process. Current methods used to estimate the underlying intensity process, such as principal component analysis, Markov chain Monte Carlo methods, and maximum likelihood estimation, are limited by memory requirements and computational times to small problems. We introduce a computationally efficient, nonparametric estimation method, REX (Reversed Exponential Smoothing) that allows for the analysis of much larger problems. We report the results of a factorial experiment to test the accuracy of these methods when the underlying intensity is generated by a higher-order autoregressive process. We assess accuracy using Kullback-Liebler Divergence[1].",
author = "Hutchison, {Kenneth M.} and Willemain, {Thomas Reed}",
year = "2013",
month = "1",
day = "1",
language = "English (US)",
pages = "2590--2599",
note = "IIE Annual Conference and Expo 2013 ; Conference date: 18-05-2013 Through 22-05-2013",

}

Hutchison, KM & Willemain, TR 2013, 'Nonparametric methods for forecasting the Cox process' Paper presented at IIE Annual Conference and Expo 2013, San Juan, Puerto Rico, 5/18/13 - 5/22/13, pp. 2590-2599.

Nonparametric methods for forecasting the Cox process. / Hutchison, Kenneth M.; Willemain, Thomas Reed.

2013. 2590-2599 Paper presented at IIE Annual Conference and Expo 2013, San Juan, Puerto Rico.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Nonparametric methods for forecasting the Cox process

AU - Hutchison, Kenneth M.

AU - Willemain, Thomas Reed

PY - 2013/1/1

Y1 - 2013/1/1

N2 - A Cox process is a Poisson process whose parameter is itself a stochastic process. Current methods used to estimate the underlying intensity process, such as principal component analysis, Markov chain Monte Carlo methods, and maximum likelihood estimation, are limited by memory requirements and computational times to small problems. We introduce a computationally efficient, nonparametric estimation method, REX (Reversed Exponential Smoothing) that allows for the analysis of much larger problems. We report the results of a factorial experiment to test the accuracy of these methods when the underlying intensity is generated by a higher-order autoregressive process. We assess accuracy using Kullback-Liebler Divergence[1].

AB - A Cox process is a Poisson process whose parameter is itself a stochastic process. Current methods used to estimate the underlying intensity process, such as principal component analysis, Markov chain Monte Carlo methods, and maximum likelihood estimation, are limited by memory requirements and computational times to small problems. We introduce a computationally efficient, nonparametric estimation method, REX (Reversed Exponential Smoothing) that allows for the analysis of much larger problems. We report the results of a factorial experiment to test the accuracy of these methods when the underlying intensity is generated by a higher-order autoregressive process. We assess accuracy using Kullback-Liebler Divergence[1].

UR - http://www.scopus.com/inward/record.url?scp=84900333405&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84900333405&partnerID=8YFLogxK

M3 - Paper

SP - 2590

EP - 2599

ER -

Hutchison KM, Willemain TR. Nonparametric methods for forecasting the Cox process. 2013. Paper presented at IIE Annual Conference and Expo 2013, San Juan, Puerto Rico.