Moment generating functions help

In summary: Do I need to see if the series converges or find the partial sum?If you are calculating the sum, then you need to see if the series converges.
  • #1
Mark53
93
0

Homework Statement


[/B]
Let X be a random variable with support on the positive integers (1, 2, 3, . . .) and PMF f(x) = C2 ^(-x) .

(a) For what value(s) of C is f a valid PMF?
(b) Show that the moment generating function of X is m(t) = Ce^t/(2− e^t) , and determine the interval for t for which it is valid. (You may use your value for C calculated in question 1, if you would like).
(c) Using the MGF, calculate the expected value and the variance of X.

The Attempt at a Solution


[/B]
a)

sum from -∞ to ∞ of C/(2^x)=1

C(1/2+1/4...)=1
C=1 as it converges

b)

m(t)=E[e^tx]=integral from -∞ to ∞ of ((e^tx)*(2^(-x)))
=integral from -∞ to ∞ of (e^tx)/(2^x)

is this the right way to go about calculating it?
 
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  • #2
Mark53 said:

Homework Statement


[/B]
Let X be a random variable with support on the positive integers (1, 2, 3, . . .) and PMF f(x) = C2 ^(-x) .

(a) For what value(s) of C is f a valid PMF?
(b) Show that the moment generating function of X is m(t) = Ce^t/(2− e^t) , and determine the interval for t for which it is valid. (You may use your value for C calculated in question 1, if you would like).
(c) Using the MGF, calculate the expected value and the variance of X.

The Attempt at a Solution


[/B]
a)

sum from -∞ to ∞ of C/(2^x)=1

C(1/2+1/4...)=1
C=1 as it converges

b)

m(t)=E[e^tx]=integral from -∞ to ∞ of ((e^tx)*(2^(-x)))
=integral from -∞ to ∞ of (e^tx)/(2^x)

is this the right way to go about calculating it?
No. For a discrete random variable we have
$$E f(X) = \sum_x p(x) f(x), $$
so involves summation, not integration.
 
  • #3
Ray Vickson said:
No. For a discrete random variable we have
$$E f(X) = \sum_x p(x) f(x), $$
so involves summation, not integration.

when calculating the sum I get:

the sum of x=0 to ∞ of (e^tx)/(2^x)=e^t/2

which is wrong am I still missing something?
 
  • #4
Mark53 said:
when calculating the sum I get:

the sum of x=0 to ∞ of (e^tx)/(2^x)=e^t/2

which is wrong am I still missing something?
Yes: you are basically saying that ##\sum_{k=0}^{\infty} r^k = r, ## which is wrong.
 
  • #5
Ray Vickson said:
Yes: you are basically saying that ##\sum_{k=0}^{\infty} r^k = r, ## which is wrong.
Do I need to see if the series converges or find the partial sum?

not sure how to start solving it
 

1. What are moment generating functions?

Moment generating functions are mathematical tools used in probability and statistics to describe the properties of a probability distribution, specifically its moments. It is a function that generates moments of a random variable, which can be used to calculate its mean, variance, and other statistical properties.

2. How do moment generating functions help in statistical analysis?

Moment generating functions help in statistical analysis by providing a way to calculate the moments of a random variable. These moments, such as mean and variance, are important in describing the shape, spread, and other characteristics of a probability distribution. Moment generating functions also allow for the derivation of other useful functions, such as the moment-generating function for the sum of two independent random variables.

3. Can moment generating functions be used for any type of probability distribution?

Yes, moment generating functions can be used for any type of probability distribution, as long as the distribution has finite moments. This includes commonly used distributions such as the normal, binomial, and exponential distributions.

4. How are moment generating functions related to characteristic functions?

Moment generating functions and characteristic functions are both mathematical tools used in probability and statistics. While moment generating functions are used to describe the moments of a probability distribution, characteristic functions describe the Fourier transform of the probability distribution. These two functions are related through a mathematical relationship called the Fourier inversion theorem.

5. Are moment generating functions only used in theoretical statistics or do they have practical applications?

Moment generating functions have both theoretical and practical applications in statistics. In theoretical statistics, moment generating functions are used to prove important theorems and derive properties of probability distributions. In practical applications, moment generating functions are used for statistical modeling and analysis, such as in actuarial science, finance, and engineering.

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