Skip to main content

Questions tagged [quasi-likelihood]

In GLMs, quasi-likelihood estimation is a way to allow over- or under-dispersion by choosing an appropriate variance function.

Filter by
Sorted by
Tagged with
1 vote
0 answers
21 views

Calculate weight for GLM-quasi poisson model

I am running several models with the quasi-Poisson family. I am looking at data from vulture restaurants. Vulture count was modelled at each site as a function of either a linear or quadratic effect ...
Emeline AUDA's user avatar
0 votes
0 answers
15 views

How to derive standard errors of regressors in quasi-Poisson regression?

Suppose I want to relax a Poisson Regression to allow for overdispersion and apply a quasi-likelihood approach: $$E[y_i|x_i] = exp(x_i^T \beta)$$ $$Var[y_i|x_i] = \phi \cdot \mu_i$$ In other words, ...
Marlon Brando's user avatar
0 votes
0 answers
18 views

Diagnostics and assumptions for Fractional Logistic Regression

I am investigating the effects of Variable X on Y'. Y' is a bounded, non-negative integer. So I have divided Y' by its upper bound to obtain a fraction Y which is in [0,1]. I am following a study ...
NutellaMonster's user avatar
6 votes
3 answers
997 views

The statistical model equivalent to this R formula

This is how my model is written in R: glm(formula = prop ~ A * B * C * D , family = quasibinomial, data = data, weights = w) This is a quasibinomial ...
ie86's user avatar
  • 355
0 votes
0 answers
9 views

Overdispersion Mixed generalized linear model

I am running a mixed generalized linear model to analyze insect capture in a baited trap. The experiment consisted of 3 separate cages, in each one one treatment (C+, C- or T) and 10 insects were ...
Andrea88's user avatar
0 votes
0 answers
43 views

Given that quasibinomial regression models extra-binomial variation, why ever do binomial regression if quasibinomial is more flexible?

In reading about quasibinomial regression: The quasi-binomial distribution, while similar to the binomial distribution, has an extra parameter 𝜙 (limited to |𝜙|≤min{𝑝/𝑛,(1−𝑝)/𝑛} ) that attempts ...
JElder's user avatar
  • 999
0 votes
0 answers
23 views

Coefficients in quasibinomial regression and model prediction plots

I'm using two quasibinomial models. In the first model, the dependent variable is the proportion x of successes in experiment A. In the second model, the dependent variable is the proportion y of ...
statuser's user avatar
0 votes
0 answers
37 views

Profile (quasi-)likelihood score tests

Suppose I have a log-likelihood or quasi-log-likelihood for my data in terms of the parameter vectors $\theta$ and $\psi$: $$L(\theta;\psi)=\frac{1}{T}\sum_{t=1}^T{\log{f(y_t|\theta;\psi)}}.$$ (I am ...
cfp's user avatar
  • 535
1 vote
0 answers
66 views

Can I use quasi-binomial regression on proportion data in this way?

prop.pass = proportion of students who passed the exam num = number of students who sat the exam ...
Jess's user avatar
  • 21
0 votes
0 answers
64 views

Poisson fixed effects model in pglm estimating time-invariant predictor

I am analyzing panel data on various municipalities (id_mun) over several years using the pglm package in R. My dataset contains a variable "treatment" which is continuous but is time-...
Mark F's user avatar
  • 1
0 votes
0 answers
69 views

Quasibinomial GLMM with LASSO regularization in R

I am currently assessing drivers of deforestation using a GLM (generalised linear model) with LASSO regularization (using package glmnet in R). As the response variable is % of area deforested I have ...
user392406's user avatar
2 votes
0 answers
25 views

Does binomial regression weight some observations more heavily than other?

I am performing a quasibinomial regression, where each subject has an unfixed number of trials. So one subject may have had 5 trials while another had 90. In R the regression equation follows: glm(...
Matt's user avatar
  • 91
0 votes
0 answers
138 views

Computing log-posterior for large variance priors

Let's say that some quantity is modelled by a time-dependent Poisson distribution, $$ y(t) \sim \text{Pois}(\mu(t)) $$ where $$ \mu(t) = \alpha_0 \exp(-\alpha_1 e^{-\alpha_2 t}) $$ and $\alpha_k > ...
Mathieu Rousseau's user avatar
0 votes
0 answers
60 views

Approximating profile likelihood confidence intervals when I only have a score function and not a likelihood

I'm working on a modeling problem where I can define a score function that looks a lot like a binomial likelihood, but the model isn't really binomial. I'd like to use profile likelihood to estimate ...
famulare's user avatar
  • 362
2 votes
1 answer
66 views

Likelihood ratio test for model specification with boundary Null

I am interested in understanding the asymptotic distribution of Likelihood ratio (LR) test statistic for model specification. I am focusing on the case in which the null hypothesis is of the form (i.e....
Alfonso's user avatar
  • 21
2 votes
3 answers
746 views

Poisson model for non-integer

I have a GLM with (quasi)poisson family. My dataset has 3 variables: rate_data rate_benchmark X So fitting the model: ...
Overkill123's user avatar
0 votes
1 answer
54 views

Why does using proportion DV (vs. count) change the results?

I'm running a regression model using a count variable as the DV and I'm trying to understand what changes when using it as a count outcome vs. a proportion. REF: https://ademos.people.uic.edu/...
llbia's user avatar
  • 351
2 votes
1 answer
44 views

Can I apply quasi-Poisson regression on discontinuous data?

I'm trying to relate the concentrations of certain air pollutants to the incidence of a disease, but I don't have continuous data on that pollution. I have daily data on cases and deaths from the ...
user379040's user avatar
1 vote
0 answers
231 views

Interpretation of p-values in glm() and sjPlot::tab_model() for quasi-poisson regression

I am running a quasi-poisson regression predicting a count outcome from 4 variables of interest. I understand that these estimates need to be exponentiated to correctly interpret the results. I used ...
stilesb's user avatar
  • 31
2 votes
1 answer
150 views

Comparing two quasipoisson glm models

Have these results but not sure how to interpret them to pick the best fitting model, I think the high p value suggests I should go with model 1? Also in terms of the order that I put the models in, ...
user avatar
1 vote
1 answer
114 views

Bnomial (Logit) regression for proportion/percentage data

I have run a binomial (logit) regression on some proportion data as the dependent variable in an Interrupted Time Seies Analysis [see below]: ...
j.rahilly's user avatar
2 votes
0 answers
327 views

What are the assumptions of fractional response regression model?

I employed fractional response regression proposed by Wooldridge and Papke for my study as the dependent variable is a proportion (remaining/whole) and is between 0 to 1 (including 0 and 1). I want to ...
Laiy's user avatar
  • 245
2 votes
0 answers
54 views

Is Quasi-Likelihood As "Strong" As The Standard Likelihood?

I have been trying to learn more about the differences between Quasi Likelihood compared to the standard Likelihood. When learning about this, the following points come to mind: The Quasi-Likelihood ...
stats_noob's user avatar
1 vote
1 answer
206 views

WHy is the over dispersion in this poisson and quasi-poisson the same?

I have a zero inflated count data, on which I have run a poisson and quasi poisson reg using glm(). The output from a poisson model is as follows: ...
Rabin KC's user avatar
1 vote
1 answer
61 views

Terminology question regarding a certain "partial maximum likelihood" which approximates the marginal likelihood

Suppose that we have a model with many parameters, which we'll partition into two subvectors called $\theta$ and $\lambda$. In this situation, $\lambda$ corresponds to those parameters that are really ...
Mike Battaglia's user avatar
2 votes
0 answers
573 views

Fitting a regression model on bounded data

Typically, in a regression equation, the dependent variable is unbounded i.e. follows normal distribution. But sometimes it may happen that dependent variable is bounded i.e. dependent variable is ...
Bogaso's user avatar
  • 871
1 vote
0 answers
32 views

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 ...
RGG's user avatar
  • 73
2 votes
0 answers
407 views

Serious Coding Error in QIC function in geepack? [closed]

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 ...
Brian M.'s user avatar
0 votes
1 answer
98 views

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 ...
neuroandstats's user avatar
1 vote
1 answer
211 views

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?
shani's user avatar
  • 681
0 votes
0 answers
55 views

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 ...
malek-il-mathematik's user avatar
0 votes
2 answers
876 views

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 ...
doublea's user avatar
2 votes
0 answers
501 views

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 ...
KPMGGMC's user avatar
  • 31
4 votes
1 answer
296 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: ...
Derek Corcoran's user avatar
1 vote
1 answer
105 views

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 ...
Babu Rao's user avatar
1 vote
1 answer
130 views

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 ...
InTheSearchForKnowledge's user avatar
2 votes
1 answer
179 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 ...
Maverick Meerkat's user avatar
2 votes
1 answer
207 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 ...
Julian Pineda's user avatar
3 votes
2 answers
762 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 ...
Julian Pineda's user avatar
0 votes
1 answer
176 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 ...
Mauro Carolli's user avatar
0 votes
1 answer
265 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 ...
Econometrics33's user avatar
3 votes
1 answer
421 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 ...
Rico's user avatar
  • 152
0 votes
0 answers
59 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 ...
Tom's user avatar
  • 528
2 votes
1 answer
1k 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 ...
Tom's user avatar
  • 528
1 vote
0 answers
201 views

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 ...
Tom's user avatar
  • 528
1 vote
0 answers
730 views

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 ...
Tom's user avatar
  • 528
1 vote
2 answers
577 views

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 ...
Tom's user avatar
  • 528
3 votes
2 answers
873 views

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: ...
Tom's user avatar
  • 528
0 votes
1 answer
222 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 ...
Francisco Javier García Sanz's user avatar
0 votes
0 answers
25 views

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(...
tobinz's user avatar
  • 101