๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Predictions in Overdispersed Series of Counts Using an Approximate Predictive Likelihood

โœ Scribed by PHILIPPE LAMBERT

Publisher
John Wiley and Sons
Year
1997
Tongue
English
Weight
191 KB
Volume
16
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

โœฆ Synopsis

The generalized autoregression model or GARM, originally used to model series of non-negative data measured at irregularly spaced time points (Lambert, 1996a), is considered in a count data context. It is ยฎrst shown how the GARM can be expressed as a GLM in the special case of a linear model for some transform of the location parameter. The Butler approximate predictive likelihood (Butler, 1986, Rejoinder) is then used to deยฎne likelihood prediction envelopes. The width of these intervals is shown to be slightly wider than the Fisher (1959, pp. 128ยฑ33) and Lejeune and Faulkenberry (1982) predictive likelihood-based envelopes which assume that the parameters have ยฎxed known values (equal to their maximum likelihood estimates). The method is illustrated on a small count data set showing overdispersion.

๐Ÿ“œ SIMILAR VOLUMES

Predicting benthic counts in Lake Huron
Predicting benthic counts in Lake Huron using spatial statistics and quasi-likelihood
โœ David M. Dolan; Abdel H. El-Shaarawi; Trefor B. Reynoldson ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 187 KB ๐Ÿ‘ 1 views

Spatial statistics have been applied to many types of problems in the environmental sciences, mostly dealing with continuously distributed data from Gaussian or near-Gaussian processes. There is a need for methods capable of handling discrete, non-Gaussian data, such as species counts from biologica

An empirical model for propagation-loss
An empirical model for propagation-loss prediction in indoor mobile communications using the Padรฉ approximant
โœ Simone G. C. Fraiha; Josiane C. Rodrigues; Regiane N. S. Barbosa; Hermรญnio S. Go ๐Ÿ“‚ Article ๐Ÿ“… 2005 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 407 KB

## Abstract This paper presents a new model for propagationโ€loss prediction in indoor mobile communications. It uses the freeโ€space loss model added to an empiric function (based on the Padรฉ approximant), and a term that describes the signal randomness (typical of the RF propagation). In order to v