Linked Questions

1 vote
1 answer

In GLM, do we make an assumption on the distribution of Y or the distribution of Y | X? [duplicate]

I just want to clarify that in GLM we make the assumption that Y | X follows some sort of distribution, not Y. For example, in the classical simple linear model, we assume that Y | X is normally ...
confused's user avatar
  • 3,263
2 votes
0 answers

What difference (if any) exists between the Response Distribution and Error Distribution in GLMs? [duplicate]

Ok, forgive my ignorance, but I keep getting confused about something at the core of GLMs. Some textbooks describe the two main parts of a GLM as the link function and the distribution of the error ...
user42719's user avatar
  • 329
0 votes
1 answer

Distribution of errors in GLMs [duplicate]

I've been recently learning about GLM's after learning about ordinary linear regression. In simple linear regression, I believe that an error term is used in order to account for randomness in the ...
Sushiix's user avatar
0 votes
0 answers

GLM distributions and link functions concept [duplicate]

There is something that i couldn't actually understand about GLMs. In wikipedia it says "(GLM) is a flexible generalization of ordinary linear regression that allows for response variables that ...
anıl ateşsaçan's user avatar
394 votes
12 answers

Difference between logit and probit models

What is the difference between Logit and Probit model? I'm more interested here in knowing when to use logistic regression, and when to use Probit. If there is any literature which defines it using ...
Beta's user avatar
  • 6,366
32 votes
2 answers

Why do we model noise in linear regression but not logistic regression?

The canonical probabilistic interpretation of linear regression is that $y$ is equal to $\theta^Tx$, plus a Gaussian noise random variable $\epsilon$. However, in standard logistic regression, we don'...
kennysong's user avatar
  • 1,061
22 votes
3 answers

How does logistic regression use the binomial distribution?

I'm trying to understand how logistic regression uses the binomial distribution. Let's say I'm studying nest success in birds. The probability of a nest being successful is 0.6. Using the binomial ...
luciano's user avatar
  • 14.5k
17 votes
1 answer

Does Poisson Regression have an error term?

I was just wondering if Poisson regression has an error term? Can a Poisson regression have random effects and an error term? I am confused about this point. In logistic regression, there is no error ...
phil12's user avatar
  • 1,381
8 votes
4 answers

Why GLM don't have an error term and why shouldn't residuals be i.i.d?

I've read dozens on post on the subject but I cannot figure this out. From what I've gathered, GLMS don't include an error term in their formulation unlike linear models (LM). I was wondering why (or ...
Boussens-Dumon Grégoire's user avatar
2 votes
2 answers

Using GLM on a continuous response variable

Let's say that I am developing a glm on a continuous response variable. I've read a number of tutorials on glm and the estimation that it utilizes. However, I'm a little lost on the specifications ...
amathew's user avatar
  • 449
4 votes
0 answers

Family in GLM - how to choose the right one?

When modeling data sampled in the field, I often come across the problem of determining the Family of the dependent variable for GLM (or GLMM). An example: in an ecological study, I have ~ 60 patches. ...
yenats's user avatar
  • 427
2 votes
1 answer

How can I test for autocorrelated errors in logistic regression?

I'm doing a Bayesian logistic regression $Y \sim X$ where my predictor $X$ is a count observed over time. So $Y$ and $X$ are each $m x n$ matrices where $m$ is the number of subjects and $n$ is the ...
Patrick's user avatar
  • 393
2 votes
2 answers

Decide which distribution function to use in GLM for a complex response variable?

I have a problem and have been searching for similar issues for the past weeks with no luck.. hopefully someone could assist me. As part of my research, I'm trying find the factors most affecting weed ...
Roni Gafni's user avatar
0 votes
0 answers

raw residual in GLM does not make sense? [duplicate]

The raw residual is defined as $r_i = y_i - \hat{y}_i$. Why raw residual does not make sense in GLM? Why do we have to standardize it to Pearson residuals?
WCMC's user avatar
  • 1,058