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-Dominant- [34]
3 years ago
13

Let X1,..., Xn be a simple random sample from a distribution with density function Svorvo-1 O<331 fx (2:0) = otherwise where

e > 0 is an unknown parameter.
(a) Find a MOM estimator for 0.
(b) If the observations are 1 1 1 2'3'2 Determine the point estimate with the estimator you find in part (a).
Mathematics
1 answer:
scZoUnD [109]3 years ago
6 0

Answer:

The method of moment (MOM) estimator as:  \mathbf{\hat {\theta} =(\dfrac{\overline X}{1-\overline X})^2}

\overline X = \dfrac{4}{9}

\mathbf{\hat {\theta} =\dfrac{16}{25} }

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.

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

E(X) = \int ^1_0 \sqrt{\theta} \ x^{\sqrt{\theta}} \ dx

E(X) = \dfrac{\sqrt{\theta} }{\sqrt{\theta} +1 } ( x ^{\sqrt{\theta}+1})^1_0

E(X) = \dfrac{\sqrt{\theta} }{\sqrt{\theta} +1 }

suppose E(X) = \overline X

Then;

\overline X = \dfrac{\sqrt{\theta} }{\sqrt{\theta} +1 }

\dfrac{1}{\overline X} = \dfrac{\sqrt{\theta} +1  }{\sqrt{\theta}}

\dfrac{1}{\overline X} =1 +  \dfrac{1}{\sqrt{\theta}}

making \dfrac{1}{\sqrt{\theta}} the subject of the formula, we have:

\dfrac{1}{\sqrt{\theta}} =\dfrac{1}{\overline X}  - 1

\dfrac{1}{\sqrt{\theta}} =\dfrac{1-\overline X}{\overline X}

\sqrt{\theta} =\dfrac{\overline X}{1-\overline X}

squaring both sides, we have:

The method of moment (MOM) estimator as:  \mathbf{\hat {\theta} =(\dfrac{\overline X}{1-\overline X})^2}

b) If the observations are \dfrac{1}{2}, \dfrac{1}{3}, \dfrac{1}{2}

Then,

\overline X = \dfrac{\dfrac{1}{2}+ \dfrac{1}{3}+\dfrac{1}{2}}{3}

\overline X = \dfrac{\dfrac{3+2+3}{6}}{3}

\overline X = \dfrac{\dfrac{8}{6}}{3}

\overline X = \dfrac{8}{6} \times \dfrac{1}{3}

\overline X = \dfrac{8}{18}

\overline X = \dfrac{4}{9}

Finally, the point estimate of the estimator \theta is

\mathbf{\hat {\theta} =\begin {pmatrix} \dfrac{\dfrac{4}{9}}{1-\dfrac{4}{9}} \end {pmatrix}^2}

\mathbf{\hat {\theta} =\begin {pmatrix} \dfrac{\dfrac{4}{9}}{\dfrac{5}{9}} \end {pmatrix}^2}

\mathbf{\hat {\theta} =\begin {pmatrix} \dfrac{4}{5} \end {pmatrix}^2}

\mathbf{\hat {\theta} =\dfrac{16}{25} }

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