Statistical modeling of daily rainfall occurrences

James A. Smith

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


In this paper, likelihood‐based inference procedures for discrete point process models are developed, and a new family of discrete point process models for daily rainfall occurrences is proposed. The model, which is termed a Markov Bernoulli process, can be viewed as a sequence of Bernoulli trials with randomized success probabilities. Contained within the family of Markov Bernoulli models are Markov chain and Bernoulli trial models. Asymptotic properties of maximum likelihood estimators of Markov Bernoulli model parameters are derived. These results provide the basis for assessing standard errors and correlation of parameter estimators and for developing likelihood ratio tests to choose among Markov Bernoulli, Markov chain, and Bernoulli trial models. Inference procedures are applied to a data set from Washington, D.C.

Original languageEnglish (US)
Pages (from-to)885-893
Number of pages9
JournalWater Resources Research
Issue number5
StatePublished - May 1987

All Science Journal Classification (ASJC) codes

  • Water Science and Technology


Dive into the research topics of 'Statistical modeling of daily rainfall occurrences'. Together they form a unique fingerprint.

Cite this