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kjetil b halvorsen
kjetil b halvorsen
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I was recently trying to understand generalized linear models (GLMs) and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

Thanks!

I was recently trying to understand generalized linear models (GLMs) and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

Thanks!

I was recently trying to understand generalized linear models (GLMs) and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

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YCor
YCor
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Generalized Linear Modelslinear models: What's the benefit of the underlying theory?

I was recently trying to understand GLMsgeneralized linear models (GLMs) and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

Thanks!

Generalized Linear Models: What's the benefit of the underlying theory?

I was recently trying to understand GLMs and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

Thanks!

Generalized linear models: What's the benefit of the underlying theory?

I was recently trying to understand generalized linear models (GLMs) and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

Thanks!

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Manuel Huppertz
Manuel Huppertz
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Generalized Linear Models: What's the benefit of the underlying theory?

I was recently trying to understand GLMs and after investing quite a few days, it still hasn't dawned on me what the fundamental benefit of the framework is. Normally, I am used to results like guarantees of convergence, limits for error etc, but all that seems to be missing here.

There is a common framework with underlying distribution, regressors/predictors linear in the coefficients, link functions and finally MLE but it seems to be branching off very quickly into the various subclasses, which each need a separate algebraical and numerical treatment.

So can anyone point me towards what is "general" about the GLMs and what is the benefit of that?

Thanks!