If X1,X2,........,Xn is a random sample taken from a population having at(0,theta) distribution, then unbiased estimator of theta
"E_p\\bar{X}=\\dfrac{1}{n}(EX_1+EX_2+.....+EX_n)=\\dfrac{1}{n}(p+p+...+p)=p"
Thus, "\\bar{X}" is an unbiased estimator for p. In this circumstance, we generally write "\\hat{p}" instead of "\\bar{X}." In addition, we can use the fact that for independent random variables, the variance of the sum is the sum of the variances to see that
"Var(\\hat{p})=\\dfrac{1}{n^2}(Var(X_1)+Var(X_2)+...+Var(X_n))"
"=\\dfrac{1}{n^2}(p(1-p)+p(1-p)+...+P(1-p))=\\dfrac{1}{n}p(1-p)"
Comments
Leave a comment