Results 271 to 280 of about 18,948 (296)

Modeling Overdispersion in R

2015
The book Overdispersion Models in SAS by Morel and Neerchal (2012) discusses statistical analysis of categorical and count data which exhibit overdispersion, with a focus on computational procedures using SAS. This document retraces some of the ground covered in the book, which we abbreviate throughout as OMSAS, with the objective of carrying out ...
Raim, Andrew M., Neerchal, Nagaraj K., Morel, Jorge G.   +2 more
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Modelling overdispersed Poisson data

2022
This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
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Modelling Overdispersion for Complex Survey Data

International Statistical Review, 2001
SummaryThe population characteristics observed by selecting a complex sample from a finite identified population are the result of at least two processes: the process which generates the values attached to the units in the finite population, and the process of selecting the sample of units from the population.
Molina, E.A., Smith, T.M.F., Sugden, R.A.   +2 more
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Overdispersion Tests in Count-Data Analysis

Psychological Reports, 2008
Count data are commonly assumed to have a Poisson distribution, especially when there is no diagnostic procedure for checking this assumption. However, count data rarely fit the restrictive assumptions of the Poisson distribution. The violation of much of such assumptions commonly results in overdispersion, which invalidates the Poisson distribution ...
Jaume, Vives, Josep-Maria, Losilla, Maria-Florencia, Rodrigo, Mariona, Portell   +3 more
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Overdispersion in nuclear statistics

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1999
Abstract The modern statistical distribution theory is applied to the development of the overdispersion theory in ionizing-radiation statistics for the first time. The physical nuclear system is treated as a sequence of binomial processes, each depending on a characteristic probability, such as probability of decay, detection, etc.
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Likelihood Analyses of Overdispersed Poisson Models

Biometrics, 1997
Summary: An over-dispersed set of counts of sowbugs from 1946 has been used to illustrate Bayesian methods of fitting, but it is shown that with the priors used the results differ little from those for maximum likelihood.
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Overdispersion and cluster models

1995
Abstract As we have seen in previous chapters of this part, count data arise from the enumeration of events on individual units. If no explanatory variables, or time, distinguish among the events, they may be aggregated as counts. In fact, this can only be done legitimately for Poisson or binomial data if the events are independent.
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Overdispersed Molecular Evolution in Constant Environments

Journal of Theoretical Biology, 1993
According to recent data analysis of DNA sequences, the dispersion index, defined as the variance-to-mean ratio of the number of base substitutions in a lineage, is often much larger than unity, which is in conflict with simple Poisson processes assumed in the molecular clock hypothesis.
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Overdispersion in poisson regression

2008
Investigation of a possible relationship between air quality and human health in the community of Prince George, British Columbia was undertaken after a public opinion poll in 1972 discovered that poor air quality was the number one concern of the residents of Prince George.
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overdispersed, adj.

2023
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