Questions tagged [quasi-likelihood]
In GLMs, quasi-likelihood estimation is a way to allow over- or under-dispersion by choosing an appropriate variance function.
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How to interprete the outcome for quasi-binomial models
I am currently trying to understand the quasi-binomial model and have a rather basic question, which I seem not to be able to see an answer. The outcomes in binomial models are binary. So, 0 vs 1, car ...
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Serious Coding Error in QIC function in geepack?
I believe the QIC function in geepack has a significant error. The function appears to incorrectly specify the independence model, which is needed to calculate QIC. The function will therefore often ...
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Can the offset variable be smaller than the outcome variable in a Poisson model?
I am modeling a number of bags of local crop harvested from a village in a given harvest over time.
The village is divided into 48 communities and I have data on the total number of bags of crops from ...
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quasi-likelihood estimates of beta
working through Peter McCullagh's glm book and having a hard time with understanding quasi-likelihood. I'm working on this question below and I think I need to find the quasi-likelihood estimates and ...
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R package for spatial regression
I want to model counts of a beetle (B) as a function of three continuous dependent variables (PC1, PC2, RL), weighted by a fourth variable (W) using either poisson or quasipoisson regression with ...
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quasi likelihood for ungrouped binary data
I read in one of the textbooks that for ungrouped binary data the dispersion parameter should always be $\phi = 1$.
Do you know why it is the case?
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Find the optimal linear combination of the following estimating function / quasi-score
Consider a random variable $Y_1 \sim Bin(n,\theta)$ and $Y_2 \sim Bin(n,\theta^2)$ where $Y_1$ is independent from $Y_2$.
Consider the residuals $R_1 = Y_1 - n\theta$ and $R_2 = Y_2 - n\theta^2$. Find ...
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Appropriate Regression Model for Proportions and Rate Data
I have a problem where my dependent variable is given as a click-through rate and thus bounded [0,1]. While I have the traffic for each sample (a combination of design factors) and could reconstruct a ...
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Comparing performance of Quasi-binomial model and Beta-binomial model
I read some books in biostatistics about fitting binary date with Beta-Binomial regression model and Quasi-Binomial regression model. It proposes a setting:
Setting:
Assuming we have a sequence of ...
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Correct GLM or NLS to model exponential model with response variable with positive and negative values
I have been struggling to find the right way to model this dataset, this is a Data Frame with the dataset:
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How to Report the Results of a Quasi-Poisson Regression (APA)
What statistics (i.e. F-statistic, t-value, p-value, etc.) would be essential or desired when presenting a Quasi-Poisson regression. And if possible, how do you get these values in R if you have a ...
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how to interpret the summary of a glm poisson [duplicate]
I need a little help with the interpretation of my model's summary.
First, here is the model :
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How to calculate model weight for GLM-quasi poisson model
I am running several models with the quasi-Poisson family, I have calculated QAIC for each model but I wanted to know the weight of each individual model. I tried AICcmodavg but it did not work.
Is ...
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Quasi-likelihood and matrix dimension
Quasi-likelihood estimating equations (quasi-score function) for the estimation is as follows
$$\sum_i\frac{\partial{\mu_i^{'}}}{\partial{\beta}}V_i^{-1}(y_i-\mu_i)=0.$$
The $\frac{\partial{\mu_i^{'}}}...
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Quasi-likelihood can't be generated by any valid probability distribution
I am learning about quasi-Poisson and i'm stuck at the concept of quasi-likelihood function.
In wikipedia, it is said that:
The term quasi-likelihood function was introduced by Robert Wedderburn in ...
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Mean-variance relationship in the quasi-likelihood
I have some questions regarding the quasi-likelihood model of GLM:
I understand that one reason to use quasi-likelihood in GLM is over-dispersion. This seems to justify using the quasi-Poisson, or ...
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Logistic Regression Model does not Converge ...How do I interpret the results? [duplicate]
I ran a logistic regression (using R) but for one of my subgroups (by Race), got a warning that the Algorithm does not converge.
When I look at my data, for 800 participants, 798 are one outcome, and ...
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How to compute the gradient for a GARCH with the package rugarch in R
I am estimating a GARCH(1,1) with external regressors and the package rugarch allows me to do it easily.
However, to compute QMLE robust standard errors, I need the ...
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How to compute the sandwich variance ML estimator in R
I'm currently estimating a DCC-type model by maximum likelihood. Im using the command solnp and it return an object where I can compute the Hessian H evaluated at ...
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Test for significant differences for data between 0 and 1
I have to test for significant differences between scenarios. Data consist of the length of a segment divided by the total length of the network. They are distributed between 0 (never equal to 0) and ...
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Output in two-part Fractional Response Model (FRM) package incorrect?
I was experimenting with the Fractional Response Model (FRM) package, and decided to replicate the results using the base GLM package to better understand the theory. I am able to replicate the ...
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Quasi-likelihood function
I got stuck in the derivation of the quasi-likelihood function. Namely, given an i.i.d sample $\{Y_i,X_i \}_{i=1}^n$ with $n$ the sample size, let the conditional mean and variance functions be ...
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Comparing the marginal effect of a GLM to the OLS estimates
My question is, whether there is any way to (somewhat) compare the marginal effect of a GLM estimate to an OLS estimate. As in, "since the OLS and GLM results are very similar, I will favour OLS ...
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Comparing the marginal effects of glm output to polr output
I have a dependent variable that is technically ordinal, so I ran a ordered probit model (polr).
However, an ordered probit model does not produce any residuals ...
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How flexible is Stata's ivpois? Could I use it for a (quasi) binomial distribution?
According to this post on statalist, Stata's ivpois (an instrumental variable approach) is pretty flexible, with very little assumptions.
The problem mentioned in ...
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How to do a Control Function (CF) / Two Stage Residual Inclusion (2SRI) with an ordinal dependent variable in the first stage and a glm in the second
I am trying to use a Control Function (CF) / Two Stage Residual Inclusion (2SRI) approach, because the modeled relationship that I am trying to estimate is non-linear (my dependent variable has a ...
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Getting the (Stata) margins from fractional regression (=glm with family quasibinomial) for an ordinal variable in R
I first found this really nice Stata video on fractional regression (the dependent variable is a proportion including 0 and 1). I am especially interested in how he applies the margin approach to ...
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What is the interpretation of a glm coefficient on a dependent variable that has a % interpretation
I have a dependent variable that takes on values between 0 and 1, including 0 and 1.
The variable signifies a proportion (0 = nothing, 1 = all). I am running a model of the type:
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Changes in significance between Poisson & quasipoisson glm
I am fairly new to GLMs, and am currently practising and testing with an insurance dataset, after many tries, I am modeling the frequency (counting model of the number of claims) and I have several ...
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A problem of likelihood function in a dynamic setting?
I'm having a problem regarding perhaps conditional maximum likelihood problem, but I'm not sure.
Suppose time horizon we consider is $T=4$, our goal is to minimize the loss function
$$
\sum_{t=2}^T L(...
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Relationship between quasi-likelihood and "GLM-conjugate" models?
Suppose we have a response variable that represents proportions with a poorly defined denominator. Two ways to handle this (1) a quasibinomial model, which assumes only that the variance is ...
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Is there any sense of applying cross validation to quasibinomial variable?
We can apply without any doubts cross validation for linear models as well as for binary models.
For linear models for example we can output RMSE and MAE and Accuracy for binary models.
But I have ...
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Comparing model efficiency
I hope you all don't mind me asking this question.
I have two models :
general linear mixed effects model
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Why is my quasibinomial GLM estimator biased - Monte Carlo simulation
I'm playing with some Monte Carlo simulations to get an idea of the properties of some linear and non-linear models.
The linear OLS model in my case is specified as:
$Y_t = \beta_0 + \beta_1x+ \...
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If my data is overdispersed when binomial but not when Poisson does that alone mean I should use Poisson GLM?
Top is Poisson, bottom is Binomial
Source of variation is different people observed over a week long period. 118 people to be exact.
want to make predictions about the proportion of days/week one ...
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Avoiding data dredging with quasibinomial GLMs
Because my data is a bit overdispersed I am using quasibinomial GLM to analyse them however this means I cannot use AICs to compare my models.
I am therefore using drop1 and update functions to do ...
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Equivalence between prediction interval and hypothesis test on Poisson predicted values with 2/3 power transformation
I fitted a quasi-Poisson regression model to predict the value of a certain counting variable.
Once the predicted value $\widehat{\mu_{0}}$ was obtained, I calculated the upper limit of its prediction ...
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Overdispersion in logistic model
I'm relatively a newbie in R, and I've been trying to make a silly example of logistic regression to predict, according to Age and Sex whether someone dies of corona or not. I'm from Colombia, so my ...
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Interpreting coefficents on quasibinomial model
This might seem like a pretty basic question but I've scoured seemingly everywhere and can't get a definitive answer.
I have a response parameter "rr" bound by 0-1 which is essentially the ...
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Quasi Poisson vs Negative Binomial [duplicate]
I read in several sources that the Quasi Poisson model and the Negative Binomial, should produce (on average) the same results. I tried a simple example and, although very close to each other, the ...
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Testing for endogeneity in a negative binomial model
I'm trying to fit a negative binomial model to my data because the dependent variable exhibits overdispersion. However, one of my reviewers is insisting that I also test for endogeneity. He or she is ...
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Interpreting interaction among 2 categorical IV in quasi-poisson regression
In my dataset, I'm looking at the impacts of developmental and immune phenotypes on morbidity- specifically, I want to determine if developmental phenotype has an effect on the difference in morbidity ...
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Can we mix conclusions from Poisson and Quasi-Poisson?
Currently I'm working with ecological studies, where my response is a count variable. I need to estimate several models, each one represents a city. Afterwards I aggregate them to obtain meta-analysis ...
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Whats the difference between logistic regression and fractional response model? [duplicate]
Can anyone tell me the theory behind fractional response model, how it really works?
I wonder if the logistic regression works only with binary variable {0,1}, why when conducting a GLM with ...
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Strange output for pairwise comparisons on glm with quasi-binomial distribution
I'm new to CrossValidated - I've read up on how to ask questions properly but sorry if I do anything slightly wrong.
My data is showing whether microplastics were present or absent in the gut of fish ...
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"scale" in logistic regression
I am working on translating some R code into Python's statsmodels package, chiefly some logistic regression work that I've done, when I came across the following in ...
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Two intercepts for zero-truncated negative binomial model using VGAM
I am trying to understand the first and second intercept for the zero-truncated negative binomial regression model I estimated using VGAM. Below is my syntax:
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How can I compare coefficient values from two different count models based on the same variables?
I'm running two quasi-Poisson models with the exact same variables, but on samples from two different countries. I'm doing this because I'm particularly interested in seeing whether the relationship ...
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Why ever use a quasipoisson model instead of bootstrapped poisson GLM?
A poisson GLM and a quasipoisson regression model will given identical point estimates for the beta parameter of the linear predictor. The quasipoisson model is typically used when there is ...
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