Consider X as a binomial random variable. Show that hat p = X/n is an unbiased estimator of p. Let X_1, X_2, X_3, X_4, X_5 be a random sample from a binomial distribution with n = 10 and p unk...
Distribution Theory for Glass's Estimator of Effect size and Related Estimators Glass's estimator of effect size, the sample mean difference divided by the sample standard deviation, is studied in the context of an explicit statistical... LV Hedges - 《Journal of Educational Statistics》 被引量:...
If X is a binomial random variable, show: (a) P' = X / n is an unbiased estimator of p. b) P' = X + n / 2 is a biased estimator of p. (c) Show that the estimator P' from part (b) becomes unbiased as nA simple random ...
While the suggested estimator is unbiased, there is reason to be concerned about its precision. In this paper expressions are obtained for the variance of the unbiased estimator and the mean square error of the commonly used biased estimator. Their comparison yields conditions under which the ...
The theory of unbiased estimation plays a very important role in the theory of point estimation, since in many real situations it is of importance to obtain the unbiased estimator that will have no systematical errors (see, e.g., Fisher ( 1925 ), Stigler ( 1977 )). The problem of ...
Some nice properties of estimators are unbiased, consistent, and the minimum variance unbiased estimator. However, not one of these desirable properties is of any help in making a probability judgment about the quality or accuracy of the estimate delivered. The confidence interval is what enables ...
This trial differs in very significant ways from an AB/BA crossover trial, including the fact that for an ABAB/BABA crossover trial there is an unbiased estimator of the differential carryover that is unaffected by between-subject variation. Despite these great differences, we arrive at the ...
An estimator is either sufficient or not, unbiased or not, Bayes or not. If exact properties are impractical or not available, statisticians often rely on approximations. This chapter gives several of the most basic results from probability theory used to derive approximations. Several notions of ...
signal, that can be approximated as an MA stochastic process. An unbiased estimator is proposed, studied and compared to two other frequently used estimators of the fourth-order cumulant (natural estimator and fourth k -statistics). Statistical comparative studies are undertaken from both bias and ...
An estimator is developed here that is unbiased in the presence of heteroskedasticity. Its behavior is examined along with the traditional estimator and another known to be unbiased in the absence of heteroskedasticity. The behavior of these corrective methods is also examined when the form and ...