Ad
related to: what is a consistent estimator
Search results
Results From The WOW.Com Content Network
In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter θ 0 —having the property that as the number of data points used increases indefinitely, the resulting sequence of estimates converges in probability to θ 0.
A consistent estimator is one for which, when the estimate is considered as a random variable indexed by the number n of items in the data set, as n increases the estimates converge in probability to the value that the estimator is designed to estimate.
A consistent estimator is an estimator whose sequence of estimates converge in probability to the quantity being estimated as the index (usually the sample size) grows without bound. In other words, increasing the sample size increases the probability of the estimator being close to the population parameter.
The term consistency in statistics usually refers to an estimator that is asymptotically consistent. Fisher consistency and asymptotic consistency are distinct concepts, although both aim to define a desirable property of an estimator. While many estimators are consistent in both senses, neither definition encompasses the other.
Under the conditions outlined below, the maximum likelihood estimator is consistent. The consistency means that if the data were generated by (;) and we have a sufficiently large number of observations n, then it is possible to find the value of θ 0 with arbitrary precision.
We have two estimators for b: b 0 and b 1. Under the null hypothesis, both of these estimators are consistent, but b 1 is efficient (has the smallest asymptotic variance), at least in the class of estimators containing b 0. Under the alternative hypothesis, b 0 is consistent, whereas b 1 isn't. Then the Wu–Hausman statistic is: [6]
Police in Minnesota believe a missing Domino’s delivery driver may be experiencing a "mental health crisis" after his car was found unoccupied in Wisconsin. Shuefaub “Shue” Xiong's red ...
A sequence of estimates is said to be consistent, if it converges in probability to the true value of the parameter being estimated: ^ . That is, roughly speaking with an infinite amount of data the estimator (the formula for generating the estimates) would almost surely give the correct result for the parameter being estimated.