How to derive the sampling distribution of some statistics

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Discussion Overview

The discussion revolves around deriving the sampling distribution of the coefficient of variation and skewness for a compound distribution formed by an Erlang distribution and a geometric distribution. The focus includes theoretical aspects of statistical distributions and their properties.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • One participant presents the forms of the Erlang and geometric distributions and derives the compound distribution, including its expectation, variance, and moments.
  • The same participant questions the possibility of deriving a formula for the sampling distribution of the coefficient of variation and skewness with a sample size ##n##.
  • Other participants provide links to Wikipedia articles on the exponential distribution, suggesting these resources may address the original question.
  • A later reply expresses uncertainty about the relevance of the Wikipedia content to the specific inquiry regarding sampling distributions.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the derivation of the sampling distribution, and there is uncertainty regarding the applicability of external resources to the discussion.

Contextual Notes

The discussion lacks clarity on the specific definitions and assumptions necessary for deriving the sampling distribution, and the relevance of the provided external links remains unresolved.

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TL;DR
Given a geometric Erlang distribution, how can I derive the sampling distribution of the coefficient of variation and the skewness for a sample of size $n$.
Assume that ##T## has an Erlang distribution:
$$\displaystyle f \left(t \, | \, k \right)=\frac{\lambda ^{k }~t ^{k -1}~e^{-\lambda ~t }}{\left(k -1\right)!}$$
and ##K## has a geometric distribution
$$\displaystyle P \left( K=k \right) \, = \, \left( 1-p \right) ^{k-1}p$$
Then the compound distribution has the following form.
$$\displaystyle g \left(t \right)= \sum _{k=1}^{\infty} f \left(t \, | \, k \right)~P \left(K =k \right)=\frac{\lambda ~p }{e^{\lambda ~t ~p }}$$
with expectation:
$$\displaystyle \mu_{{1}}\, = \,{\frac {1}{\lambda\,p}}$$
variance:
$$\displaystyle \mu_{{2}}\, = \,{\frac {1}{{\lambda}^{2}{p}^{2}}}$$
and third central moment:
$$\displaystyle \mu_{{3}}\, = \, {\frac {2}{{\lambda}^{3}{p}^{3}}}$$
The coefficient of variation ##c_v## is given by:
$$\displaystyle {\it c_v}\, = \,{\frac { \sqrt{\mu_{{2}}}}{\mu_{{1}}}}=1$$
and the skewness ##\tilde{\mu}_3## by:
$$\displaystyle {\it \tilde{\mu}_3}\, = \,{\frac {\mu_{{3}}}{{\mu_{{2}}}^{3/2}}}=2$$
Is it possible to derive a formula for the sampling distribution of the coefficient of variation and the skewness with a sample size ##n##?
 
Last edited:
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mathman said:

I am now aware that ##g(t)## is in fact an exponential distribution with rate parameter ##\lambda \, p##. But the Wikipedia site on the exponential distribution makes no mention of sampling distributions.
 
Section on statistical parameters? I am not sure what you want.
 

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