# Tag Info

### AIC versus Likelihood Ratio Test in Model Variable Selection

AIC and likelihood ratio test (LRT) have different purposes. AIC tells you whether it pays to have a richer model when your goal is to approximate the underlying data generating process the best you ...
• 67.6k
Accepted

### Is the exact value of any likelihood meaningless?

It's “meaningless” in the sense that it's very hard to interpret, it's just “the bigger, the better”. That is the case because the likelihood is not probability and it is calculated without ...
• 138k

### Likelihood ratio vs. score vs. Wald test: Different p values, which to use?

First, I disagree somewhat with jsakaluk's answer that the two tests are testing different things - they are both testing whether the coefficient in the larger model is zero. They are just testing ...
• 1,035
Accepted

Accepted

### GLM tests involving deviance and likelihood ratios

The confusion probably comes from the fact that there are three models involved, and the term "deviance" refers to twice the log or the likelihood ratio between two of them. The models are: ...
• 23.8k
Accepted

### Likelihood Ratio Test statistic for the exponential distribution

This is one of the cases that an exact test may be obtained and hence there is no reason to appeal to the asymptotic distribution of the LRT. To see this, begin by writing down the definition of an ...
• 20.4k

### Does likelihood ratio test control for overfitting?

Your reasoning is too pessimistic. Given the $K$ additional features, the LR test statistic will follow an asymptotic $\chi^2$ distribution with $K$ degrees of freedom if the null is true (and other ...
• 33.3k

• 3,930
Accepted

### Likelihood ratio test for random intercept (MATLAB)

Is there a way of generating a LinearMixedModel object which does not include any random effects? I am not a MATLAB expert, and programming questions are off-topic here anyway (so you might try ...
• 60.9k
Welcome here. A simple solution is to define a new binary variables which takes the value $1$ for the subset of the data and $0$ for all other observations not included in the subset. Then you can ...