Questions tagged [dglm]
Questions about double generalized linear models, that is, models simultaneuosly fitting a model for the mean and another model for the variance, and quastions about implementations of such models, for instance R package `dglm`. **NOT** for dynamic generalized linear models!
7 questions
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Inconsistency in statistics: parameters estimation with heteroskedasticity
I want to estimate this model
$$
y_t = a + b x_t + \sigma x_t\epsilon_t
$$
where we have an error with heteroskedasticity (it depends on $x_t$).
Suppose I estimate this model with OLS so, assuming ...
- 31
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2
answers
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Parameter estimation of Gamma Distribution using R
This question might also be suited to the programming site, but I thought since there is enough on the statistics side, I would use this forum. I am trying to estimate the alpha parameter in a Gamma ...
- 53
3
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1
answer
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Maximum likelihood estimation under heteroskedasticity (and relation to OLS)
I have a question about MLE and how it relates to OLS. I know how to relate OLS and MLE when the noise is normal and homoskedastic. I can apply the same reason for heteroskedastic noise. My question ...
- 485
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Simulate linear regression with heteroscedasticity
I am trying to simulate a dataset that matches empirical data that I have, but am unsure how to estimate the errors in the original data. The empirical data includes heteroscedasticity, but I am not ...
- 511
3
votes
1
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Distribution of dispersion submodel
In double generalized linear models where we assume $y$ follows an exponential dispersion model, where the mean can be modelled as
$$g(\mu_i)=x_i^T\beta,$$
and the dispersion $(\phi)$ can be modelled ...
- 133
9
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1
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Fitting a heteroscedastic generalized linear model for binomial responses
I have data from the following experimental design: my observations are counts of the numbers of successes (K) out of corresponding number of trials (...
- 463
22
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2
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Using R for GLM with Gamma distribution
I currently have a problem understanding the syntax for R for fitting a GLM using the Gamma distribution.
I have a set of data, where each row contains 3 co-variates ($X_1, X_2, X_3$), a response ...
- 605