Answer:
The method of moment (MOM) estimator as: ![\mathbf{\hat {\theta} =(\dfrac{\overline X}{1-\overline X})^2}](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%28%5Cdfrac%7B%5Coverline%20X%7D%7B1-%5Coverline%20X%7D%29%5E2%7D)
![\overline X = \dfrac{4}{9}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B4%7D%7B9%7D)
![\mathbf{\hat {\theta} =\dfrac{16}{25} }](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%5Cdfrac%7B16%7D%7B25%7D%20%7D)
Step-by-step explanation:
From the question, the correct format for the probability density function is:
![fx(x ; \theta) = \left \{ {{\sqrt{\theta x}^{\sqrt{\theta}-1}}\ \ 0 \leq x \leq 1 \atop {0} \ \ \ \ \ \ \ otherwise } \right.](https://tex.z-dn.net/?f=fx%28x%20%3B%20%5Ctheta%29%20%3D%20%5Cleft%20%5C%7B%20%7B%7B%5Csqrt%7B%5Ctheta%20x%7D%5E%7B%5Csqrt%7B%5Ctheta%7D-1%7D%7D%5C%20%5C%20%200%20%5Cleq%20x%20%5Cleq%20%201%20%5Catop%20%7B0%7D%20%5C%20%20%5C%20%20%20%5C%20%5C%20%20%5C%20%5C%20%20%5C%20otherwise%20%7D%20%5Cright.)
where θ > 0 is an unknown parameter.
(a) The MOM estimator can be calculated as follows:
![E(X) = \int ^1_0x. \sqrt{\theta} \ x^{\sqrt{\theta}-1} \ dx](https://tex.z-dn.net/?f=E%28X%29%20%3D%20%5Cint%20%5E1_0x.%20%5Csqrt%7B%5Ctheta%7D%20%5C%20x%5E%7B%5Csqrt%7B%5Ctheta%7D-1%7D%20%5C%20dx)
![E(X) = \int ^1_0 \sqrt{\theta} \ x^{\sqrt{\theta}} \ dx](https://tex.z-dn.net/?f=E%28X%29%20%3D%20%5Cint%20%5E1_0%20%5Csqrt%7B%5Ctheta%7D%20%5C%20x%5E%7B%5Csqrt%7B%5Ctheta%7D%7D%20%5C%20dx)
![E(X) = \dfrac{\sqrt{\theta} }{\sqrt{\theta} +1 } ( x ^{\sqrt{\theta}+1})^1_0](https://tex.z-dn.net/?f=E%28X%29%20%3D%20%5Cdfrac%7B%5Csqrt%7B%5Ctheta%7D%20%7D%7B%5Csqrt%7B%5Ctheta%7D%20%2B1%20%7D%20%28%20x%20%5E%7B%5Csqrt%7B%5Ctheta%7D%2B1%7D%29%5E1_0)
![E(X) = \dfrac{\sqrt{\theta} }{\sqrt{\theta} +1 }](https://tex.z-dn.net/?f=E%28X%29%20%3D%20%5Cdfrac%7B%5Csqrt%7B%5Ctheta%7D%20%7D%7B%5Csqrt%7B%5Ctheta%7D%20%2B1%20%7D)
suppose E(X) = ![\overline X](https://tex.z-dn.net/?f=%5Coverline%20X)
Then;
![\overline X = \dfrac{\sqrt{\theta} }{\sqrt{\theta} +1 }](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B%5Csqrt%7B%5Ctheta%7D%20%7D%7B%5Csqrt%7B%5Ctheta%7D%20%2B1%20%7D)
![\dfrac{1}{\overline X} = \dfrac{\sqrt{\theta} +1 }{\sqrt{\theta}}](https://tex.z-dn.net/?f=%5Cdfrac%7B1%7D%7B%5Coverline%20X%7D%20%3D%20%5Cdfrac%7B%5Csqrt%7B%5Ctheta%7D%20%2B1%20%20%7D%7B%5Csqrt%7B%5Ctheta%7D%7D)
![\dfrac{1}{\overline X} =1 + \dfrac{1}{\sqrt{\theta}}](https://tex.z-dn.net/?f=%5Cdfrac%7B1%7D%7B%5Coverline%20X%7D%20%3D1%20%2B%20%20%5Cdfrac%7B1%7D%7B%5Csqrt%7B%5Ctheta%7D%7D)
making
the subject of the formula, we have:
![\dfrac{1}{\sqrt{\theta}} =\dfrac{1}{\overline X} - 1](https://tex.z-dn.net/?f=%5Cdfrac%7B1%7D%7B%5Csqrt%7B%5Ctheta%7D%7D%20%3D%5Cdfrac%7B1%7D%7B%5Coverline%20X%7D%20%20-%201)
![\dfrac{1}{\sqrt{\theta}} =\dfrac{1-\overline X}{\overline X}](https://tex.z-dn.net/?f=%5Cdfrac%7B1%7D%7B%5Csqrt%7B%5Ctheta%7D%7D%20%3D%5Cdfrac%7B1-%5Coverline%20X%7D%7B%5Coverline%20X%7D)
![\sqrt{\theta} =\dfrac{\overline X}{1-\overline X}](https://tex.z-dn.net/?f=%5Csqrt%7B%5Ctheta%7D%20%3D%5Cdfrac%7B%5Coverline%20X%7D%7B1-%5Coverline%20X%7D)
squaring both sides, we have:
The method of moment (MOM) estimator as: ![\mathbf{\hat {\theta} =(\dfrac{\overline X}{1-\overline X})^2}](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%28%5Cdfrac%7B%5Coverline%20X%7D%7B1-%5Coverline%20X%7D%29%5E2%7D)
b) If the observations are ![\dfrac{1}{2}, \dfrac{1}{3}, \dfrac{1}{2}](https://tex.z-dn.net/?f=%5Cdfrac%7B1%7D%7B2%7D%2C%20%5Cdfrac%7B1%7D%7B3%7D%2C%20%5Cdfrac%7B1%7D%7B2%7D)
Then,
![\overline X = \dfrac{\dfrac{1}{2}+ \dfrac{1}{3}+\dfrac{1}{2}}{3}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B%5Cdfrac%7B1%7D%7B2%7D%2B%20%5Cdfrac%7B1%7D%7B3%7D%2B%5Cdfrac%7B1%7D%7B2%7D%7D%7B3%7D)
![\overline X = \dfrac{\dfrac{3+2+3}{6}}{3}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B%5Cdfrac%7B3%2B2%2B3%7D%7B6%7D%7D%7B3%7D)
![\overline X = \dfrac{\dfrac{8}{6}}{3}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B%5Cdfrac%7B8%7D%7B6%7D%7D%7B3%7D)
![\overline X = \dfrac{8}{6} \times \dfrac{1}{3}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B8%7D%7B6%7D%20%5Ctimes%20%5Cdfrac%7B1%7D%7B3%7D)
![\overline X = \dfrac{8}{18}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B8%7D%7B18%7D)
![\overline X = \dfrac{4}{9}](https://tex.z-dn.net/?f=%5Coverline%20X%20%3D%20%5Cdfrac%7B4%7D%7B9%7D)
Finally, the point estimate of the estimator
is
![\mathbf{\hat {\theta} =\begin {pmatrix} \dfrac{\dfrac{4}{9}}{1-\dfrac{4}{9}} \end {pmatrix}^2}](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%5Cbegin%20%7Bpmatrix%7D%20%5Cdfrac%7B%5Cdfrac%7B4%7D%7B9%7D%7D%7B1-%5Cdfrac%7B4%7D%7B9%7D%7D%20%5Cend%20%7Bpmatrix%7D%5E2%7D)
![\mathbf{\hat {\theta} =\begin {pmatrix} \dfrac{\dfrac{4}{9}}{\dfrac{5}{9}} \end {pmatrix}^2}](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%5Cbegin%20%7Bpmatrix%7D%20%5Cdfrac%7B%5Cdfrac%7B4%7D%7B9%7D%7D%7B%5Cdfrac%7B5%7D%7B9%7D%7D%20%5Cend%20%7Bpmatrix%7D%5E2%7D)
![\mathbf{\hat {\theta} =\begin {pmatrix} \dfrac{4}{5} \end {pmatrix}^2}](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%5Cbegin%20%7Bpmatrix%7D%20%5Cdfrac%7B4%7D%7B5%7D%20%5Cend%20%7Bpmatrix%7D%5E2%7D)
![\mathbf{\hat {\theta} =\dfrac{16}{25} }](https://tex.z-dn.net/?f=%5Cmathbf%7B%5Chat%20%7B%5Ctheta%7D%20%3D%5Cdfrac%7B16%7D%7B25%7D%20%7D)