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Questions tagged [underdispersion]

Underdispersion is when there is less variability than there 'ought' to be in the data. Eg, the variance of counts could be less than the mean, whereas the variance of a Poisson should equal the mean.

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Is there a single discrete distribution that handles over and under dispersion? [duplicate]

I have some count data I am trying to model. The variance is very close to the mean, so the Poisson distribution for the entire data set seems like a good starting point. I have done and it seems to ...
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Residual Deviance and degrees of freedom - Negative Binomial Distribution

I am trying to model count data using python's statsmodels module (Beer's sold at a football stadium as function of visitors, "tilskuer", and weather data). ...
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fitting COM-Poisson in R

I have some crash data I did Poisson for that and the data was underdispersed. I want to do COM-Poisson regression for my data. I see that every website suggest several packages for COM-Poisson and I'...
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Is there a common underdispersed discrete distribution with unbounded support for general mean and variance?

I have a mean $\mu$ and a variance $\sigma^2$ with underdispersion, i.e., $\sigma^2<\mu$. Is there a standard discrete distribution with these moments and unbounded-on-the-right support, i.e., ...
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VGAM plotting predictions and confidence intervals genpoisson/generalized poisson

I fit a generalized poisson using VGAM and can output predictions using predict. However, the fitted.values are a matrix, ...
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1answer
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Interpreting dispersion for Inverse Gaussian GAM (log linked)

After reading Wood (2006), Zuur et al. (2009) and all questions related to GAMs here, I still haven't found the following: Should I calculate the dispersion for an ...
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Name of the test for over/under dispersion

When testing for over or underdispersion in a count variable there is a test (available for example in the glm.nb() function in the ...
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Dealing with “underdispersion” in binomial GLMM

I try to fit several binomial glmms. My interest is wheather historic and recent samples differ in their climatic conditions. My data is organized as follows, where ...
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763 views

Causes for Underdispersion in Poisson Regression

I am working with count data (number of pregnancies per woman), and using glm Poisson (log-link) to model determinants of the former count variable. From simple descriptives I observe that my data ...
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Quasi-poisson for underdispersed data

Related to glm() in R, I saw a few post recommending modeling underdispersed data using the Conway–Maxwell–Poisson distribution, specifically with the R package <...
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What distribution for Golf strokes?

I'm trying to find a distribution that adequately represents the number of strokes professional golfers make on a par 3 hole, so that I can simulate outcomes from this distribution. I have been ...
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1answer
427 views

How to address underdispersion in a GLMM

I've read a few entries about underdispersion in count or binomial data, relating to poisson and binomial probability distributions. However, I haven't been able to find any information about gaussian ...
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Sampling from under/over-dispersed count data in R

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population. For each type, I build ...
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383 views

How to define the nu parameter of Conway-Maxwell-Poisson in spaMM package

I am trying to model some count data (clutch size) which are underdispersed. I want to account for different fixed and random (intercept) effects. My initial model was by using a random effect Poisson ...
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1answer
441 views

Forecasting a distribution for count data

I'm working on a project to forecast the distribution of a baseball player's "At Bats per game" (a baseball statistic w/ integer domain) using a player's position in his team's batting order as a ...
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Can I ignore under-dispersion in my count data?

I have under-dispersed count data. I do not want to transform them, and using a negative binomial error distribution (via glmer.nb) does not help. My results are the same regardless of the ...
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Does binomial distribution have the smallest possible variance among all “reasonable” distributions that can model binary elections?

Imagine an election where $n$ people make a binary choice: they vote for A or against it. The outcome is that $m$ people vote for A, and so A's result is $p=m/n$. If I want to model these elections, ...
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GLM for proportional data and underdispersion

I'd like asking your help to understand a statistical issue from my data set. I ran a GLM with proportional data, using a binomial distribution. However, I've found underdispersion in my model and I ...
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Overdispersion and Underdispersion in Negative Binomial/Poisson Regression

I was performing a Poisson regression in SAS and found that the Pearson chi-squared value divided by the degrees of freedom was around 5, indicating significant overdispersion. So, I fit a negative ...
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Poisson regression with underdispersed and truncuated/censored upper bound

I'm analysing data from an experiment in which participants, over a number of trials, were presented with 8 boxes - 7 containing gold coins, and 1 containing a pirate. Their task was to open as many ...
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Are these data underdispersed? If so, what mechanisms may explain this?

Say someone who is well practiced (appears to have reached a performance plateau) shoots 20 free throws on 15 different days and is successful the number of times shown in the upper histogram (...
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How to deal with underdispersion with binomial data

I'm working with a pretty large dataset (n = 4,500) where 10% of my points (pixels in a GIS landscape) are 1s and the rest are 0s. The full model for my data looks something like this: ...
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How to handle underdispersion in GLMM (binomial outcome variable)

I'm working on the following model in R: ...
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What is the correct way to identify overdispersion?

I calculated the dispersion parameters for each of two blocks in a hierarchical logistic regression. I am not quite sure how to interpret the dispersion parameter. First I calculated the parameters ...
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1answer
251 views

How to know dispersion if $\mu$ is close to or below 0 (chance-corrected beta-binomial model)

Background In sensory science, "replication" means having a panelist in a taste panel do multiple rounds of the same test. You cannot just count those additional rounds as additional panelists, ...
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Reason to worry if the emp. residual distribution is more dense around zero compared to a theoretical normal?

My goal was to evaluate, if a marketing scheme did benefit or not. I observed data about the price ($P_t$) of a specific product over time. Since my dependend variable is only defined on $[0,\infty)$...
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What is the appropriate model for underdispersed count data?

I am trying to model count data in R that is apparently underdispersed (Dispersion Parameter ~ .40). This is probably why a glm with ...
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Underdispersed count data and factor analysis

I am working with count data from a psychological experiment. Data are from several subtests, i.e. scores on these tests. The way these scores are generated is clearly not Gaussian (not only because ...