Hostname: page-component-f554764f5-wjqwx Total loading time: 0 Render date: 2025-04-08T16:26:23.148Z Has data issue: false hasContentIssue false
  • English
  • Français

Statistical inference for point-process models of rainfall

Published online by Cambridge University Press:  01 July 2016

James A. Smtih  and
Alan F. Karr
James A. Smtih
Affiliation:
Interstate Commission on the Potomac River Basin, Rockville
Alan F. Karr
Affiliation:
The Johns Hopkins University
Get access Rights & Permissions [Opens in a new window]

Extract

In this paper we develop maximum likelihood procedures for parameter estimation and hypothesis testing for three classes of point processes that have been used to model rainfall occurrences; renewal processes, Neyman-Scott processes, and RCM processes (which are members of the family of Cox processes). The statistical inference procedures developed in this paper are based on the intensity process

Type
Applied Probability in Biology and Engineering. An ORSA/TIMS Special Interest Meeting
Information
Advances in Applied Probability , Volume 16 , Issue 1 , March 1984 , pp. 22
Copyright
Copyright © Applied Probability Trust 1984 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable