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 language | English (US) |
|---|---|
| Pages | 2590-2599 |
| Number of pages | 10 |
| State | Published - Jan 1 2013 |
| Event | IIE Annual Conference and Expo 2013 - San Juan, Puerto Rico Duration: May 18 2013 → May 22 2013 |
Other
| Other | IIE Annual Conference and Expo 2013 |
|---|---|
| Country | Puerto Rico |
| City | San Juan |
| Period | 5/18/13 → 5/22/13 |
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All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
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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 conference › Paper
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].
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M3 - Paper
SP - 2590
EP - 2599
ER -