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

learn more… | top users | synonyms

0
votes
0answers
14 views

Distribution of sum of negative binomial parametrized with mean and aggregation parameter

From textbook, I know that: If each $X_i$ is distributed as negative binomial $(r_i,p)$ then $\sum X_i$ is distributed as negative binomial $(\sum r_i ,p)$. In R: ...
0
votes
0answers
9 views

Extensions of bsts and CausalImpact to non-Gaussian exponential family distributions

The bsts and CausalImpact packages implement a state space time series model with an optional regularized regression component. ...
0
votes
0answers
8 views

Comparing hurdle models to negative binomial models

I'm trying to compare the AIC or log-likelihood of a negative binomial GLM to a hurdle type approach, consisting of a binomial GLM for the presence/absence of a count and the counts modelled with a ...
4
votes
1answer
55 views

Name of rv that results from integrating over gamma in gamma product prior on poisson

If $d$ is an arbitrary random variable with parameter(s) $\Psi$ and positive support, $g \sim \mathrm{Gamma}(\alpha, \beta)$, $x \sim \mathrm{Poisson}(gd)$, and $g$ and $d$ are independent, then ...
4
votes
1answer
53 views

Sum of squared Negative Binomial probability masses

Let $(p_k)_{k=0, \dots, \infty}$ denote the probability masses of a Negative Binomial distribution with parameters $r>0$ and $p\in]0,1[$. I'm looking for the sum of their squares, ...
0
votes
1answer
33 views

Dealing with zero-inflation if the data are not count data type

In the literature I found that for the count data with a lot of zeros so-called zero-inflated distributions (models) and so-called hurdle-at-zero distributions (models) could be used. The differences ...
0
votes
0answers
18 views

How to decide whether to do regression through the origin or not (with a GLMM)?

I'm fitting a Negative Binomial GLMM (with glmmADMB), where the DV is the number of units sold of a product bundle and the IVs are count variables that indicate how many items from a specific category ...
1
vote
0answers
23 views

What is expected count formula for zero-inflated negative binomial regression?

My IT department wants me to translate my zero-inflated negative binomial regression model into a formula for calculating expected count which they can hard code into SQL. I'm running the model in ...
0
votes
1answer
31 views

How can I use negative binomial regression with caret?

I'd like to use negative binomial regression with caret. However in the list of supported models I can't find it. I tried to use: ...
1
vote
0answers
24 views

Should predictions with negative binomial regression only produce integers?

I have a dataset consisting of about 600 observations. Each observation has around 100 attributes. One of the attributes I want to predict. Since the attribute that I want to predict can only have ...
1
vote
1answer
31 views

Alternatives to Chi-Squared for Single Categorical Outcome and Single Categorical Predictor w/counts for factors [R]

I am from an applied background, where X2 and G-tests are the default ways to analyze count data (default as in, until today, I had no idea there were other ways, as I was only taught these methods). ...
3
votes
0answers
36 views

The use of the negative binomial dispersion parameter in model selection…?

I'm doing model selection, analysing the effect of a number of variables on the number of shoots browsed by deer, using the number of shoots available as an offset variable. My data distribution is ...
1
vote
1answer
21 views

Commonality analysis in negative binomial regression?

I am new to negative binomial regression and am using Generalized Linear Models in SPSS to analyze some highly skewed count data (it is not zero inflated and the variance is much higher than the mean ...
7
votes
1answer
116 views

Why is the Quasipoisson in glm not treated as a special case of Negative Binomial?

I'm trying to fit generalized linear models to some sets of count data that might or might not be overdispersed. The two canonical distributions that apply here are the Poisson and Negative Binomial ...
0
votes
0answers
15 views

Comparing different estimation approaches — poisson vs negative binomial vs FE?

What's the appropriate way to compare two different estimation approaches? I've got a panel data model (balanced with states as panels and years as time) -- but it's over dispersed with a lot of ...
1
vote
0answers
22 views

Interpreting scaled betas for quadratic terms in a negative binomial regression

I created a negative binomial model where the final model included 5 quadratic predictors (each with a corresponding linear term). I am considering two ways to interpret the beta coefficients for each ...
0
votes
0answers
16 views

High Value of Incidence-Rate Ratio

after running several negative binomial models, the IRR for one of my ivars has returned consistently high IRR values ranging from 18 to 365.... All the other ivars return 'normal' IRR values and I am ...
1
vote
0answers
45 views

Estimating probability of observing greater than X events based on a current population and historical rates

Let's say I have a population that varies from month to month, and per month, there are X number of failures. Based on historical rates, I am trying to find the probability of observing Y or greater ...
0
votes
0answers
15 views

using mgfs to find limiting distribution

Can someone help me out with proving that the limiting distribution of a negative binomial (k,p) distributed variable is poisson if we let k -> infinity and p->1. I am just learning about limiting ...
1
vote
0answers
29 views

interpretation negative binomial

I have a question towards an analysis of purchasing decisions. I have a data set where I investigate the amount of previous purchases last week (predictor) on the amount of purchases today (response ...
0
votes
0answers
13 views

How do I model chapter-verse references?

Context: I am part of an 8-person group in which each person posts a Bible verse every day. For those who don't know, that is of the format "Psalm 30:1" where first we reference the chapter, then the ...
2
votes
1answer
203 views

Negative-binomial why the answer is 0.288?

In a negative-binomial experiment with three independent trials, if I want the probability for 2 successes before the first failure. The probability of each trial is 0.6. ...
2
votes
1answer
54 views

Poisson and NB results

i did a regression with poisson model, and since the dependant variable is overdispersed (0.13) , i have also tried negative binomial. The problem is that my explanatory variable "repeated partner" is ...
0
votes
0answers
30 views

Negative Binomial Regression 0 Problem

I am dealing with a count data set. I am trying to predict the amount of purchasers per day by the amount of previous purchasers for different products, counting the amount of purchases per day. ...
1
vote
1answer
41 views

glm.nb fails to converge when adding one zero

I have a problem where glm.nb (R version 3.1.0, MASS version 7.3.33) converges on some data, but adding only one 0 it does not converge any more. This is the data ...
0
votes
1answer
28 views

Standard error of Poisson and negative binomial regression

In Poisson and negative binomial regression, the response is assumed have Poisson and negative binomial distributions respectively. When we test the significance of the parameter $\beta$, which is ...
0
votes
0answers
38 views

Can I use SAS Copula procedure or Matlab copulafit to fit count data (Poisson or Negative Binomial)

I want to simulate data (x,y) with dependency using copula. Matlab has the function for t copula, Clayton, Frank, and Gumbel bivariate Archimedean copulas. But I am not sure if these functions could ...
1
vote
0answers
42 views

Multiple starting value/convergence warnings when running glm.nb in R

I'm running a negative binomial GLM in R. Running the model with the pooled dataset works just fine, but I'm encountering several warning messages when I attempt to run the same model for some of the ...
0
votes
0answers
65 views

WinBUGS/JAGS code for calculating Bayesian p-value from negative binomial model

I have a working negative binomial model written in BUGS code, but am not sure about the appropriate Bayesian p-value code to test goodness of fit. Specifically, I would like to calculate Pearson's ...
1
vote
0answers
21 views

How to model the following dependent variable? [closed]

If I got a dataset which the (count data) dependent variable has the following distribution, how should I model it? I am aware of the zero inflated model and the negative binomial model, but are ...
2
votes
1answer
45 views

Exploring effect of treatment on count data

I've collected data on animal visitation at four different points in time. The four time points represent the total animal visitations over a three day period, i.e. 3 days of monitoring at four ...
2
votes
1answer
39 views

Need code for sampling negative binomial with non-integer $r$

I'm trying to write code (in C) to sample from a negative binomial distribution parameterized by $r,p$, where $r$ is not necessarily integer (also called Polya distribution). I've found a number of ...
3
votes
0answers
95 views

simulating birth death process with random numbers from negative binomial

I am trying to generate random deviates for the population size at time $t$ for a birth-death process with constant birth and death rates per individual and initial size $N_0 \gt 0$. For the simple ...
2
votes
1answer
122 views

Is it meaningful to calculate predicted marginal effects of a count data model with an interaction effect?

In a little regression model of mine, I estimate the following formula a a negative binomial regression type (it would hold for a Poisson regression as well): $$ y = \beta * var1 + \gamma * var1 * ...
2
votes
1answer
39 views

Finding how many times I need to perform an action before I have an x% chance of a specific outcome in r

Non-specific question: There is an action that can be performed that has multiple outcomes. Each outcome has a different probably, p, of occurring. How many times, n, do I need to do the action before ...
1
vote
1answer
177 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 ...
0
votes
0answers
92 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. ...
0
votes
0answers
70 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 ...
0
votes
1answer
28 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 ...
2
votes
0answers
94 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 ...
1
vote
0answers
57 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. ...
0
votes
0answers
27 views

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

From a Negative Binomial regression, I obtain the following coefficients: ...
1
vote
0answers
88 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 ...
0
votes
0answers
58 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
votes
0answers
40 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 ...
3
votes
0answers
93 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. ...
2
votes
1answer
88 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 ...
1
vote
0answers
25 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 ...
3
votes
2answers
195 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
votes
1answer
40 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 ...