What is the relation between estimator and estimate? In Lehmann's formulation, almost any formula can be an estimator of almost any property There is no inherent mathematical link between an estimator and an estimand However, we can assess--in advance--the chance that an estimator will be reasonably close to the quantity it is intended to estimate
Estimator for a binomial distribution - Cross Validated How do we define an estimator for data coming from a binomial distribution? For bernoulli I can think of an estimator estimating a parameter p, but for binomial I can't see what parameters to estim
ML vs WLSMV: which is better for categorical data and why? I was wondering which is a better estimator to use for categorical data: ML or WLSMV I saw on a discussion on the Mplus website that they recommend WLSMV for categorical data but didn't explain why
r - Lavaan Estimator - Cross Validated I'm not sure either estimator is going to perform well in this scenario Your sample size is tiny in the context of SEM and whatever results generated from it are likely to greatly under-estimate the true population values You could instead consider using a Bayesian SEM with blavaan using sensible priors
Notation in statistics (parameter estimator estimate) In statistics, it is very important to differentiate between the following three concepts which are often confused and mixed by students Usually, books denote by $\\theta$ an unknown parameter Th
random variable - When is the median-of-means estimator better than the . . . As discussed in the above notes, or also in (math ST:1509 05845), the median-of-means estimator gives finite-sample exponential concentrations guarantees It is also my understanding (though I'm less certain about this) that median-of-means only provides advantages for distributions with heavy tails