May 11, 2018 · 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.

[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.

The HL median estimate is especially simple for small samples of size n, just compute all possible two point (including repeats) averages. From these n(n+1)/2 new constructs, compute the HL Median Estimator as the usual sample median. Now, per the same Wikipedia article on the median, the cited variance of the median 1/(4*n*f(median)*f(median)).

Jun 14, 2016 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Sep 15, 2018 · purpose: an estimator seeks to know a property of the true state of nature, while a prediction seeks to guess the outcome of a random variable; and. uncertainty: a predictor usually has larger uncertainty than a related estimator, due to the added uncertainty in the outcome of that random variable. Well-documented and -described predictors ...

Aug 2, 2018 · We use an estimator which books usually denote by $\widehat{\theta}$. The estimator is a random variable ...

@NRH's answer to this question gives a nice, simple proof of the biasedness of the sample standard deviation. Here I will explicitly calculate the expectation of the sample standard deviation (the original poster's second question) from a normally distributed sample, at …

Oct 7, 2011 · 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.

An estimator is unbiased if, on average, it hits the true parameter value. That is, the mean of the sampling distribution of the estimator is equal to the true parameter value. The two are not equivalent: Unbiasedness is a statement about the expected value of …