"f(x,\\theta)=\\frac{2(\\theta-x)}{\\theta^2}" , "0<x<\\theta"
"L(x,\\theta)=\\frac{2(\\theta-x)}{\\theta^2}"
"\\frac{\\partial L(x,\\theta)}{\\partial \\theta}=\\frac{-2}{\\theta^2}(1-\\frac{2x}{\\theta})"
"\\frac{-2}{\\theta^2}(1-\\frac{2x}{\\theta})=0"
"1=\\frac{2x}{\\theta}"
"2x=\\hat\\theta" Thus, the MLE of "\\theta" is 2x.
To check for biasedness, expectation of 2x shoud not be equal to theta.
"E(2x)=\\smallint_0^{\\theta}\\frac{2x(2(\\theta-x))}{\\theta^2}dx"
"=[\\frac{2x^2(3\\theta-2x)}{3\\theta^2}]_0^\\theta"
"=\\frac{2\\theta^2(3\\theta-2\\theta)}{3\\theta^2}-0"
"=\\frac{2\\theta}{3}\\ne\\theta" , Thus, the MLE (2x) is a biased estimator of theta.
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