A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.

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7 views

Choosing reasonable parameters for a negative binomial distribution

My data is a list of observations and a count for each observation. The data is overdispersed, the mean is ~1,200 and the variance is ~18,000,000. I want to use a negative binomial model to assign ...
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14 views

Zero Inflated Poisson or Negative Binomial Regression model in Panel Data

I am using the Stata version 13.1. My data is count in nature and also it is a balanced panel. However, my data suffers from the problem of excess zeros and also suffers the problem of overdispersion. ...
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0answers
21 views

Negative binomial mixed effect model for repeated measures with R - prediction and plotting

I have a dataset to analyze in which a response was recorded at the ends of months 1,3,4,5,6 in 187 patients. All patients had the responses recorded in each week, and all patients started a treatment ...
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1answer
10 views

Random Coefficient Negative Binomial Model

I have a crash count data and i want to build a random coefficient negative binomial model in R. The dependent variable will be the crash counts and covariates will be Lane width, AADT, shoulder width ...
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0answers
26 views

Modeling discrete count data with a gamma distribution

I've encountered a statistical model in which discrete count data are modeled with a gamma distribution (supported on nonnegative reals). The model relies on the property of the gamma that a sum of ...
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36 views

Negative binomial regression: Different results between the glm y x, (nbinomial 1) link(log) and glm y x, (nbinomial ml) link(log) Stata command?

I'm running a negative binomial regression. I found big differences in the results* if I compute with the glm y x, (nbinomial 1) link(log) and glm y x, (nbinomial ml) link(log) (for Stata) command. ...
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0answers
17 views

Upon transforming coefficients, how do I transform SE, z value and Pr(>|z|)?

From a Negative Binomial regression, I obtain the following coefficients: ...
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30 views

Predicted number of events from xtnbreg, fe make no sense

I am trying to compute the number of predicted events from a fixed effects negative binomial regression model in Stata. I run the model first using random effects, then using fixed effects, and ...
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13 views

Adding a square root link function to an overdispersed negative binomial GLM

I'm analyzing nematode count data (80 data points) from a randomized block design in which I have two factors with both four levels (Plant and Inoc). The data show heavy overdispersion when analyzed ...
2
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0answers
26 views

Specifying distribution in generalized estimating equation GEE

GEE allows you to identify the distribution of the outcome variable and appropriate link function. How do you make this selection in a longitudinal model where the distribution changes in time. An ...
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0answers
42 views

Modelling flight delays with negative values

Modelling flight delays with negative values I am working on a model to predict whether a flight will be delayed. The data consists of some explanatory variables for flights from a specific airport. ...
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0answers
6 views

GLM to asess differences between two treatments with only one IV

I have some data that I've collected in the field while running a trial where some sites (random 50%) received a treatment and the others did not. The dependent variable is the total number of baits ...
2
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1answer
37 views

Poisson for percentage data if values are low?

I have percentage data for diet per area (example here.....) I have no data on the individuals contributing to this diet, only for the population as a whole for each area. I want to assess ...
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0answers
15 views

Zero-inflated negative binomial regression: 0 probability of a count greater than 0

Zero-inflated negative binomial regression assumes 0s are generated by two processes: a group whose counts are generated by a negative binomial regression and a group who have a "0 probability of a ...
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2answers
97 views

Justification for using a zero-inflated negative binomial regression

I'm trying to describe in words why I used a zero-inflated negative binomial regression instead of an negative binomial regression: To model my data I used a negative binomial regression. However, as ...
0
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1answer
31 views

Distribution of number of Bernoulli trials before some large number of sucess [duplicate]

We repeatedly make an experiment where we count the number $n$ of Bernoulli trials of known probability $p$, until some number of successes $s$ is reached. I'm willing to restrict to $p<0.01$ and ...
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2answers
96 views

hurdle model with negative binomial distribution of counts - error message and model selection

I'm working with over-dispersed count data, which is zero inflated (~2/3 zeros). I've fit a hurdle model using hurdle from ...
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1answer
58 views

Computing repeatability from overdispersed zero-inflated negative binomial GLMMM in R

I'm trying to compute repeatability of a count response variable from a Generalized linear mixed model with multiple fixed effects and individual ID as a random effect. I'm dealing with both ...
3
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1answer
145 views

R glmer.nb output. How to get $\hat{\theta}$?

I would like to obtain estimated $\theta$ from glmer.nb function in lme4 package. In my understanding this function fits the model: $$ Y_{ij}|\boldsymbol{B}_{i}=\boldsymbol{b}_i \overset{ind.}{\sim} ...
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0answers
26 views

Does negative binomial regression assume sample independence?

I'm working with a negative binomial multiple regression and I'm wondering about the assumption of spatial independence of samples. White and Bennetts (1996) say that the assumption of spatial ...
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0answers
17 views

Trending Residuals in Negative Binomial Panel Regressions with Patent Data

A commonly faced problem for researchers working with patent data is that we need to work with negative binomial models because our dependent variable is an overdispersed count variable. I am using ...
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0answers
38 views

Modelling count data: mean-variance relationship

I have fit a poisson, quasi poisson, and negative binomial model to some count data. To ensure that I have valid models, I am checking that the following assumptions are satisfied: No ...
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0answers
43 views

choosing between overdispersed poisson or negative binomial regression

I am performing a GLM on count data (insurance claims) and I wish to compare Overdispersed Poisson Regression (ODP) against Negative Binomial regression. I would know whether there is a practical ...
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1answer
149 views

Feasibility of Negative Binomial Spatial Regression

I have a set of crime count data where it appears that the data take on a negative binomial distribution. I have had some success converting the dependent variable (a crime count) into a rate and then ...
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1answer
30 views

Regression formula for negative binomial with random effects

I am doing a negative binomial regression with random effects. I have Panel data on 23 countries ($i$) across 28 years ($t$), with one dependent, one independent and three control variables. I do ...
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0answers
28 views

what is the meaning and purpose of modeling a data?

Background: I collected a whole years access logs of my website, counted visit frequency for every user, and the numbers of user at each unique frequency, I got a distribution: $n_w \tilde\ D_w(f_w ; ...
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26 views

Reference on interpretation of similar observed values and average adjusted predictions

I analyzed the association between a count dependent variable (DV) and a dummy independent variable (IV) (coded 0 and 1) ...
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0answers
36 views

Did the Eggenberger-Pólya (1923) paper derive the negative binomial distribution with real-valued parameter?

The negative binomial distribution can be derived in many ways, but two famous ones are due to Greenwood & Yule (1920) and Eggenberger & Pólya (1923). Greenwood & Yule assumed unobserved ...
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50 views

Count Panel Data Event Study

Can anyone suggest a lecture/paper/textbook that covers an event study (eg. exogenous policy change) using count time-series (or penal) data? Or alternatively, just a general guideline as to what ...
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214 views

Overdispersion and modeling alternatives in Poisson random effect models with offsets

I have run into a number of practical questions when modeling count data from experimental research using a within-subject experiment. I briefly describe the experiment, data, and what I have done so ...
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1answer
43 views

Simulating Negative Binomial Arrivals

I am building a simulation. I want to generate arrivals according to a negative binomial process. The data will show the minute of each "customer" arrival and look something like the following: ...
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0answers
22 views

Convergence of distributions implies convolution (with itself) converge?

If 2 distributions converge in the limit, then do their convolutions also converge? e.g. I can show that for the Geometric distribution with random variable $T$, the scaled version with parameter ...
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1answer
39 views

What is the relationship between theta and size in negative binomial distribution?

In negative binomial regression glm.nb(y~x), I got a parameter theta and two coefficients? And then I want to use dnbinom(x, size, prob, mu, log = FALSE) to calculate the predicted probability. can ...
0
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1answer
131 views

Interpreting negative binomial regression with log transformed independent variables

My independent variables were highly skewed, so to normalise the distribution they were log transformed. Also since there were zeros in the data, I've added + 1 to transform the variables. This is ...
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0answers
102 views

Negative Binomial Regression model; algorithm did not converge

I am using R to run some negative binomial regression models. For model 1 I have the number of network in-degrees as the dependent variable, and Twitter followers, friends and number of Twitter ...
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1answer
114 views

Compare poisson and negative binomial regression with LR test

My question is related to the question Compare negative binomial models. I have some difficulties understanding UCLA guide in http://www.ats.ucla.edu/stat/r/dae/nbreg.htm (I am using ...
3
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1answer
169 views

Zero-inflated negative binomial models: why not use two separate models?

Zero-inflated negative binomial models have two components: a count component (negative binomial regression part) and a zero component (logistic regression part). Why not just run two separate ...
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0answers
33 views

How to prove NegativeBinomial(r,p) converges to Gamma(r,1) as p->0

Let $X\sim NegBin(r,p)$ and $Y\sim Gamma(r,1)$. How can I prove that $pX \overset{dist}\to Y$ as $p\to 0$. Is this statement the same as $X\overset{dist}\to Gamma(r,1/p)$. Thanks.
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1answer
78 views

Negative Binomial Distribution R

This question was given in class and I was wondering how to do this in R: "Sixty percent of a large lot of old spark plugs are still usable, and they can be individually tested to determine this. Let ...
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2answers
73 views

What to call exponentiated coefficients from a Poisson/negative binomial regression of cross-sectional data

In epidemiology, exponentiated coefficients are often reported as odds ratios, relative risks/ incidence rate ratios or hazard ratios. In the analysis of cross-sectional data using Poisson/negative ...
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0answers
100 views

R gam() throws error: “Can't correct step size”

I am computing a GAM on a large set of data sets. Almost all of them work, just this one data set makes gam() throw an error. I paste a code that reproduces this error here: ...
3
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0answers
189 views

Theoretical justification of choice for confidence interval exact method for the success probability parameter of negative binomial variable?

I have a computer experiment that runs the Bernoulli series with unknown probability $p$ of success. The experiment terminates when $m$ failures are observed. So, the unknown parameter $p$ has the ...
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0answers
136 views

Relative variable importance for glm & glm.nb - Percentage of deviance explained

I'm currently trying to calculate variable importance for multiple GLMs. I've got both continous and count data response variables. The first I modeled with gaussian GLM, the second with negative ...
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0answers
42 views

How to make beta coefficients comparable?

My study design delivers both, count data and continous outcomes (e.g., numbers of taxa vs. an diversity index). As these variables are used as response variables, I have to use negative binomial glm ...
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1answer
94 views

Why does the null deviance in glm.nb differ between models of the same response variable?

I'm struggeling to understand the topic of deviance. Let's have two models as follows: ...
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0answers
43 views

Negative binomial instead of ordinal logistic?

I have data with one metric and a discrete outcome that I'm trying to estimate the probability of. The outcome is a count, but it's a safe assumption that the increments aren't i.i.d. I thought an ...
2
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0answers
143 views

“weight” input in glm.nb function in R. How exactly does the weight affect the likelihood?

I would like to understand how the weight argument of glm.nb is affecting the likelihood function. I understand that glm.nb find the MLE in an alternating iteration process where for a given theta the ...
2
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0answers
25 views

Is there a simple model that assumes a negative relationship between the mean and variance of response?

Poisson and Negative Binomial models assume that the variance of response is greater or equal to the mean of response. Is there a simple model where that assumption is reversed, i.e. variance goes ...
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0answers
31 views

A/B testing zero inflated data

I am trying to conduct an A/B(/C) test to compare the performance of 3 different website pages but I'm facing issues regarding zero inflated data. I have data for each page regarding ...
8
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1answer
225 views

Low sample size: LR vs F - test

Some of you might have read this nice paper: O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods in Ecology and Evolution 1:118–122. klick. Currently I am comparing negative binomial ...