[A]n estimator is a rule for calculating an estimate of a given quantity [of the underlying distribution] based on observed data. The important difference is: A statistic is a function of a sample. An estimator is a function of a sample related to some quantity of the distribution. For what "Quantity" means, see section below.

2018年5月11日 · 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.

The estimator then would be the method of generating estimations, for example the method of maximum likelihood. classifier : This specifically refers to a type of function (and use of that function) where the response (or range in functional language) is discrete.

2011年10月7日 · For bernoulli I can think of an estimator estimating a parameter p, but for binomial I can't see what parameters to estimate when we have n characterizing the distribution? Update: By an estimator I mean a function of the observed data. An estimator is used to estimate the parameters of the distribution generating the data.

2020年11月19日 · The term how to find a good estimator is quite broad. Often we assume an underlying distribution and put forth the claim that data follows the given distribution. We then aim at fitting the distribution on our data. In this case ensuring we minimize the distance (KL-Divergence) between our data and the assumed distribution.

The most common estimator used for this approach is some form of diagonally weighted least squares (DWLS). WLSMV falls under the DWLS umbrella, though it is not technically an estimator. DWLS is the estimator, and calling WLSMV in a software package (e.g., lavaan or Mplus ) tells the program to report robust standard errors and to use a ...

The bootstrap will approximate the sampling distribution for the m-estimator at the given fixed sample size. It is an approximate method and we say the bootstrap works if we can show consistency theoretically. $\endgroup$

2018年3月27日 · $\begingroup$ @AlvaroFuentes Nope unfortunately. I think I asked my professor once and the response was along the lines of one of those "head nods, that is interesting" and then they give a non-answer which indicates they either don't know the answer or don't really care what the answer is.

2025年1月22日 · There is a big literature on this problem in ecology, where it is equivalent to estimating (asymptotic) species richness; the literature starts from Good-Turing estimators for the probability of encountering previously unseen tokens in a cryptographic analysis and goes on from there; most of the methods used in ecology are based on approaches pioneered by Anne Chao.

I may be asking dumb or non-sensical question, but what does the variance of an estimator for a regression parameter (e.g. $\beta_{0}, \beta_{1}$) mean? How does it even have variance? Isn't it a constant estimate of a presumed true but unknown constant value?