2. ca) Let X: { x, X2 ... Xn j be probability density sgunction : f (x; O) rundom sample rom the distri bution with öther wise where e>0 is an untn own peri meter : mcx (X, X2,., Xn) is the mcximum likelihoud i. Given thot 6: Xm) : estimator 0. Determine the bics of of of esti mctor Ii. Derive the unbicsed estimgtor X cm), denoting this of that e by trens yorming an unbiased esti mabor of estima tor é. Show e and derive its variance. Ii. Is the estimalor obtained in par t ii G consis tent estimctor of you Justi sy your ans wer.

MATLAB: An Introduction with Applications
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2. Ca) Let
X: { x, Xe .. Xn } be
rundom sample om the distri bution with
probe bility density sunction :
for
f (x; @) .
bther wise
where e>o is an
untnown
peri meter
0 : X cn) : mcx (x, X2,., X) is the mcximum likelihoud
of o. Determine the bias
i. Given thot
estimator
of
esti mctor
of
Ө.
Il. Derive the unbicsed estimg tor
cs) , denoting
furming X cn)
an unbiased esti mabor
of
this
of
that e
estima tor ē. Show
e and derive
its variance .
III. Is the estimator
you
obtuined in
part ii a consis tent estimctor
Justi sy your ans wer.
Transcribed Image Text:2. Ca) Let X: { x, Xe .. Xn } be rundom sample om the distri bution with probe bility density sunction : for f (x; @) . bther wise where e>o is an untnown peri meter 0 : X cn) : mcx (x, X2,., X) is the mcximum likelihoud of o. Determine the bias i. Given thot estimator of esti mctor of Ө. Il. Derive the unbicsed estimg tor cs) , denoting furming X cn) an unbiased esti mabor of this of that e estima tor ē. Show e and derive its variance . III. Is the estimator you obtuined in part ii a consis tent estimctor Justi sy your ans wer.
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