Presence of greater variability in a data set than would be expected
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In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given statistical model.
A common task in applied statistics is choosing a parametric model to fit a given set of empirical observations. This necessitates an assessment of the fit of the chosen model. It is usually possible to choose the model parameters in such a way that the theoretical population mean of the model is approximately equal to the sample mean. However, especially for simple models with few parameters, theoretical predictions may not match empirical observations for higher moments. When the observed variance is higher than the variance of a theoretical model, overdispersion has occurred. Conversely, underdispersion means that there was less variation in the data than predicted. Overdispersion is a very common feature in applied data analysis because in practice, populations are frequently heterogeneous (non-uniform) contrary to the assumptions implicit within widely used simple parametric models.
In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given statistical...
that the observed variance is greater than the mean; this is known as overdispersion and indicates that the model is not appropriate. A common reason is...
distribution of it, known as phylogenetic overdispersion (Webb et al., 2002). The reverse of phylogenetic overdispersion is phylogenetic clustering in which...
biodiversity research for analyzing count data where overdispersion is very common. This is because overdispersion is indicative of biological aggregation, such...
distribution; otherwise, it simply has a wide peak. Further, it will exhibit overdispersion relative to a single normal distribution with the given variation. Alternatively...
Compound distributions are useful for modeling outcomes exhibiting overdispersion, i.e., a greater amount of variability than would be expected under...
where a solute comes out of solution in the form of floccules or flakes Overdispersion or statistical aggregation, where the variance of a distribution is...
probability distributions. If τ exceeds 1, the model is said to exhibit overdispersion. The binomial case may be easily extended to allow for a multinomial...
subpopulations (due to spread from different means), and thus exhibits overdispersion relative to a normal distribution with fixed variation σ , {\displaystyle...
violated, in particular when the range of count values is limited or when overdispersion is present. Index of dispersion Empirical distribution function Frequency...
linear models, such as quasi-likelihood and a quasibinomial model; see Overdispersion § Binomial. In modern computers, binary data refers to any data represented...
goodness of fit of the model; for example, grouped data may exhibit overdispersion relative to the variance estimated from the ungrouped data. A binary...
of probability Outline of regression analysis Outline of statistics Overdispersion Overfitting Owen's T function OxMetrics – software p-chart p-rep P-value...
especially if there is un-modelled residual auto-correlation or un-modelled overdispersion. Cross-validation can be used to detect and/or reduce overfitting problems...
variance is always bigger than the expected value. This property is called overdispersion. This is in contrast to the Poisson distribution where mean and variance...
negative multinomial distribution (NM(k, p)) allowing for heterogeneity or overdispersion to the probability vector. It is used in quantitative marketing research...
for the reduction in potential reproductive partners by an increased overdispersion in the vertebrate host the reduced egg hatching time in the external...
concentrate chemotherapy on heavily infected individuals (taking advantage of overdispersion) and to improve sanitation to reduce the rate of transmission. Wharton...
Quasi-likelihood estimation is particularly useful when there is overdispersion. Overdispersion occurs when there is more variability in the data than there...
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