Let X1,X2,.....,Xn be random sample of size n from a distribution with probability
density function
f(X; ,0)={ (θX) to the power (θ-1) ,0<X<1 , θ>0 Obtain a maximum likeyhood
0, elsewhere.
estimator of θ.
"f(X,\\theta)=\\theta X^{\\theta-1}, 0\\le X\\le1, \\theta>0"
"L(\\theta)=\\prod_{i=1}^nf(X,\\theta)"
"=\\theta ^n\\prod_1^n(Xi)^{\\theta-1}"
"\\ln L(\\theta)=n\\ln\\theta+(\\theta-1)\\ln \\prod_1^n Xi"
To maximize "\\ln L(\\theta)", we equate the first derivative of "\\ln L(\\theta)"to zero and solve for "\\theta"
"\\frac{d}{d\\theta}\\ln L(\\theta)=\\frac{n}{\\theta}+\\ln \\prod_1^n Xi"
"\\frac{n}{\\theta}+\\ln \\prod_1^n Xi=0"
"\\frac{n}{\\theta}=-\\ln \\prod_1^n Xi"
"\\hat{\\theta}=-\\frac{n}{\\prod_1^nXi}"
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