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a method of estimating parameters of a statistical model by choosing the parameter value that optimizes the probability of observing the given sample.
8
votes
Accepted
Parameter estimation of Gamma Distribution using R
You can compute MLE for the gamma distribution using the dglm package, which is available from the CRAN repository. Here is an example run. Note that the two parameters being estimated in this example …
5
votes
Does Fisher scoring exist as such?
Yes, certainly it exists and there is no problem with its implementation.
Fisher scoring is one of the most commonly-used algorithms in statistics and is it usually implemented exactly as defined in y …
5
votes
Why does the glm residual deviance have a chi-squared asymptotic null distribution?
I assume that you are referring to the total residual deviance that is computed when you fit a generalized linear model.
Your question alludes to a widespread misconception. Regardless of what you mig …
5
votes
Accepted
Log-transformation in negative log-likelihood for negative binomial distribution
The MLE of $p$ is available in closed form. There is no need to run an algorithm to maximize the likelihood.
The negative binomial is an example of an linear exponential family and, for all linear exp …
3
votes
Calculate the MLEs for betas of a logistic regression
In this regression, then are only two observations and two fitted values, which are $\hat p_m=17/91$ for males and $\hat p_f=32/109$ for females.
Converting to the logit linear predictor scale gives: …
3
votes
Iterative optimization of alternative glm family
Parametric link functions
The "softplus" model you have proposed is called a parametric link function in generalized linear model theory. Technically, the link function is the inverse of your softplu …
3
votes
Accepted
Is the prior in Bayes formula a probability or it can also represent a probability distribut...
Bayes formula assumes that there is a distribution over every quantity, so there is no need to rewrite it.
Bayes formula applies to both discrete and continuous random quantities. If the quantities ar …
1
vote
Accepted
Maximum likelihood estimators for means of independent normal random variables where means d...
This problem would seem to have a simple solution, or a number of simple solutions.
The unconstrained MLEs of the $\mu_i$ are simply $\hat\mu_1=\bar x$, $\hat\mu_2=\bar y$ and $\hat\mu_3=\bar z$.
You …