- #1
Mogarrr
- 120
- 6
Imo, this problem is crazy hard.
Let X have the negative binomial distribution with pmf:
[itex] f_X(x) = \binom{r+x-1}{x}p^{r}(1-p)^{x}[/itex], x=0.1.2...,
where [itex]0<p<1[/itex] and r is a positive integer.
(a) Calculate the mgf (moment generating function) of X.
(b) Define a new random variable by Y=2pX. Show that as [itex]p \downarrow 0[/itex], the mgf of Y converges to that of a chi squared random variable with 2r degrees of freedom by showing that
[itex] lim_{p \to 0} M_Y(t) = (\frac 1{1-2t})^{r}, |t|< \frac 12[/itex].
the moment generating function for a discrete random variable, denoted as [itex]M_X(t)[/itex] is equal to
[itex] \sum_x e^{tx}f_X(x) [/itex]
So I think I've figured out part (a), but I'm stuck on part (b).
For part (a) the mgf is
[itex]\sum_{x=0}^{\infty} e^{tx} \binom{r+x-1}{x}p^{r}(1-p)^{x} = \sum_{x=0}^{\infty} \binom{r+x-1}{x}p^{r}(e^{t}(1-p))^{x} [/itex].
I would think that...
[itex]\sum_{x=0}^{\infty} \binom{r+x-1}{x}(1-e^{t}(1-p))^{r}(e^{t}(1-p))^{x} = 1[/itex],
since this is another negative binomial pmf (probability mass function), whose sum must be 1.
So now I do my sneaky trick...
[itex] \frac {(1-(1-p)e^{t})^{r}}{(1-(1-p)e^{t})^{r}} \frac {p^{r}}{p^{r}} \sum_{x=0}^{\infty} \binom{r+x-1}{x}p^{r}(e^{t}(1-p))^{x} = \frac {p^{r}}{(1-(1-p)e^{t})^{r}} \cdot \sum_{x=0}^{\infty} \binom{r+x-1}{x}(1-e^{t}(1-p))^{r}(e^{t}(1-p))^{x} =
\frac {p^{r}}{(1-(1-p)e^{t})^{r}}[/itex].
So if this is correct, that takes care of part (a).
For part (b) I have [itex] Y = 2pX [/itex], so [itex] y = 2px \iff x = \frac {y}{2p} [/itex]. I think this is a bijection, so I have
[itex] f_Y(y) = \sum_{x \in g^{-1}(y)} f_X(x) = f_X(\frac {y}{2p}) [/itex].
I'm not too sure about this transformation, but continuing on, I have mgf of Y is
[itex] \sum_y e^{ty}f_Y(y) = \sum_y e^{yt} \binom {r + \frac {y}{2p} - 1}{\frac {y}{2p}}p^{r}(1-p)^{\frac {y}{2p}} [/itex]...
I could continue on, but the trick I used earlier doesn't seem to work on this summation. Please help.
Homework Statement
Let X have the negative binomial distribution with pmf:
[itex] f_X(x) = \binom{r+x-1}{x}p^{r}(1-p)^{x}[/itex], x=0.1.2...,
where [itex]0<p<1[/itex] and r is a positive integer.
(a) Calculate the mgf (moment generating function) of X.
(b) Define a new random variable by Y=2pX. Show that as [itex]p \downarrow 0[/itex], the mgf of Y converges to that of a chi squared random variable with 2r degrees of freedom by showing that
[itex] lim_{p \to 0} M_Y(t) = (\frac 1{1-2t})^{r}, |t|< \frac 12[/itex].
Homework Equations
the moment generating function for a discrete random variable, denoted as [itex]M_X(t)[/itex] is equal to
[itex] \sum_x e^{tx}f_X(x) [/itex]
The Attempt at a Solution
So I think I've figured out part (a), but I'm stuck on part (b).
For part (a) the mgf is
[itex]\sum_{x=0}^{\infty} e^{tx} \binom{r+x-1}{x}p^{r}(1-p)^{x} = \sum_{x=0}^{\infty} \binom{r+x-1}{x}p^{r}(e^{t}(1-p))^{x} [/itex].
I would think that...
[itex]\sum_{x=0}^{\infty} \binom{r+x-1}{x}(1-e^{t}(1-p))^{r}(e^{t}(1-p))^{x} = 1[/itex],
since this is another negative binomial pmf (probability mass function), whose sum must be 1.
So now I do my sneaky trick...
[itex] \frac {(1-(1-p)e^{t})^{r}}{(1-(1-p)e^{t})^{r}} \frac {p^{r}}{p^{r}} \sum_{x=0}^{\infty} \binom{r+x-1}{x}p^{r}(e^{t}(1-p))^{x} = \frac {p^{r}}{(1-(1-p)e^{t})^{r}} \cdot \sum_{x=0}^{\infty} \binom{r+x-1}{x}(1-e^{t}(1-p))^{r}(e^{t}(1-p))^{x} =
\frac {p^{r}}{(1-(1-p)e^{t})^{r}}[/itex].
So if this is correct, that takes care of part (a).
For part (b) I have [itex] Y = 2pX [/itex], so [itex] y = 2px \iff x = \frac {y}{2p} [/itex]. I think this is a bijection, so I have
[itex] f_Y(y) = \sum_{x \in g^{-1}(y)} f_X(x) = f_X(\frac {y}{2p}) [/itex].
I'm not too sure about this transformation, but continuing on, I have mgf of Y is
[itex] \sum_y e^{ty}f_Y(y) = \sum_y e^{yt} \binom {r + \frac {y}{2p} - 1}{\frac {y}{2p}}p^{r}(1-p)^{\frac {y}{2p}} [/itex]...
I could continue on, but the trick I used earlier doesn't seem to work on this summation. Please help.