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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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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1answer
32 views
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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65 views
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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19 views
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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1answer
22 views
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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1answer
42 views
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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1answer
66 views
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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35 views
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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1answer
80 views
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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1answer
81 views
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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35 views
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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89 views
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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36 views
Intuition for reasoning between Quasi-poisson and Negative Binomial regression
I am aware of the several similar questions existing here (like this, this or this), but my question is slightly different and remains after going through most of those posts. Specifically, my ...
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29 views
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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Do we have to check heteroscedasticity when estimating quasibinomial model?
As we all know we are not interested in checking heteroscedasticity when estimating logit model when our variable is binary (taking only values 0 and 1). However do we also don't have to test ...
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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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GLM on features selected by PLS-DA
First question: Can we use GLM on specific variables selected by PLS-DA latent components? To obtein p-value of response of prediction (e.g.for each selecetd variable of comp 1)?
Second question: What ...
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1answer
62 views
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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128 views
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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41 views
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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Should "kenward-roger"/"satterthwaite" degrees of freedom be used with "GLMM" fitted with penalized quasilikelihood"? [duplicate]
Should kenward-roger degrees of freedom correction be used with generalized linear mixed models fitted with penalized quasilikelihood/"PQL"?
There's no reason it isn't implementable but the ...
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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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345 views
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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1answer
154 views
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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1answer
221 views
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 conlcusions 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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1answer
206 views
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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1answer
86 views
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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1answer
223 views
"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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119 views
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:
mod.negb <- vglm(ED_Visit ~ Male + ...
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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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1answer
143 views
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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Quasi-binomial GLM in R
I'd like some advice on data I'm analyzing from a factorial-design study in which each sample is a count of 200 urchin eggs that were exposed to various types and concentrations of pollutants, and for ...
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How to Calculate Sample Size Requirements for Quasi-Poisson / Negative Binomial AB Test in R
I'm doing an AB test of two conditions, where the success metric is looking for an increase in the number of users taking a particular action each day. The variance is hugely larger than the mean, ...
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523 views
Does poisson penalized quasi likelihood regression use biased estimators?
A professor told me the Poisson glmmPQL (mixed-effects/hierarchical) regression gives biased estimates.
The paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886992/ from PLOS One says that ...
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337 views
What is the distribution in Quasi-Poisson regression?
For Poisson regression, the assumption is that Y has a Poisson distribution. Is the same assumption true for Quasi-Poisson regression?
