a. n Let X₁,..., X be a random sample from a uniform distribution on [0, 0]. Then the mle of is ô = Y = max(X;). Use the fact that Y≤y iff each X; ≤y to derive the cdf of Y. Then show that the pdf of Y = max(X) is nyn-1 Ꮎn 0 fy(y) = { 0≤ y ≤ 0 otherwise b. Use the result of part (a) to show that the mle is biased but that (n + 1)max(X;)/n is unbiased.

A First Course in Probability (10th Edition)
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ISBN:9780134753119
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Chapter1: Combinatorial Analysis
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a.
n
Let X₁,..., X be a random sample from a uniform
distribution on [0, 0]. Then the mle of 0 is
ô = Y = max(X;). Use the fact that Y≤y iff each
X; ≤y to derive the cdf of Y. Then show that the pdf
of Y = max(X) is
fx(v)
nyn-1
Ꮎn
0
=
ľ
0≤ y ≤ 0
otherwise
b. Use the result of part (a) to show that the mle is
biased but that (n + 1)max(X;)/n is unbiased.
Transcribed Image Text:a. n Let X₁,..., X be a random sample from a uniform distribution on [0, 0]. Then the mle of 0 is ô = Y = max(X;). Use the fact that Y≤y iff each X; ≤y to derive the cdf of Y. Then show that the pdf of Y = max(X) is fx(v) nyn-1 Ꮎn 0 = ľ 0≤ y ≤ 0 otherwise b. Use the result of part (a) to show that the mle is biased but that (n + 1)max(X;)/n is unbiased.
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