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
What is the difference between a consistent estimator and an unbiased . . . 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 the sampling distribution of the estimator
What is the difference between estimation and prediction? 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
Variance of sample median - Cross Validated 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))
Why do we estimate mean using MLE when we already know that mean is . . . This is a lot better than trying to figure out a unique estimator for each type of data and then stepping lots of time worrying if it's really the best choice In short: while MLE doesn't provide new insight in the case of estimating the mean of normal data , it in general is a very, very useful tool
time series - How to estimate the autocorrelation function? - Cross . . . Usually we are concerned with the overall performance of an estimator as judged by a metric like MSE Bias contributes to this, but it is not the entire story In time-series analysis, the auto-correlation of observations makes it difficult to obtain unbiased estimators, so there is often a fall-back to biased estimators that are nonetheless
ML vs WLSMV: which is better for categorical data and why? 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