Poisson Hidden Markov Models for Time Series of Overdispersed Insurance Counts

Hidden Markov models for time series of counts with excess zeros

Integer-valued time series are often modeled with Markov models or hidden Markov models (HMM). However, when the series represents count data it is often subject to excess zeros. In this case, usual distributions such as binomial or Poisson are unable to estimate the zero mass correctly. In order to overcome this issue, we introduce zero-inflated distributions in the hidden Markov model. The em...

استفاده از مدل مارکوف پنهان در پیش‎بینی موارد جدید سل در استان همدان بر اساس اطلاعات موارد ثبت شده طی سال‎های 94-1384

Background and Objectives: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality. It is caused by a Mycobacterium tuberculosis. Awareness of the incidence and number of new cases of the disease is valuable information for revising the implemented programs and development indicators. time series and regression are commonly used models for prediction but these m...

Multivariate Poisson Hidden Markov Models for Analysis of Spatial Counts

Statistical models for overdispersion in the frequency of peaks over threshold data for a flow series

[1] In a peaks over threshold analysis of a series of river flows, a sufficiently high threshold is used to extract the peaks of independent flood events. This paper reviews existing, and proposes new, statistical models for both the annual counts of such events and the process of event peak times. The most common existing model for the process of event times is a homogeneous Poisson process. T...

Parametric Models of Linguistic Count Data

It is well known that occurrence counts of words in documents are often modeled poorly by standard distributions like the binomial or Poisson. Observed counts vary more than simple models predict, prompting the use of overdispersed models like Gamma-Poisson or Beta-binomial mixtures as robust alternatives. Another deficiency of standard models is due to the fact that most words never occur in a...