Expectation of a Negative Binomial RV

In summary, the conversation discusses finding the expected value of a Negative Binomial random variable using first principles. The formula for the probability mass function of Y is provided, and the attempt at a solution involves manipulating factorials. The conversation also mentions using the sum of geometric random variables to find the expectation, but the problem must be solved using first principles. The question of why this implies Y is proper is also mentioned, but the answer is not known.
  • #1
richievuong
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Homework Statement


Consider a Negative Binomial random variable Y ~ NB(r, p). Show (from first principles!) that E[Y] is r/p. Why does this imply Y is proper?


Homework Equations


I have no idea how to use latex, so this may be messy:

pmf of Y: [ (k+r-1)! / k!(r-1)! ] * (1-p)^r * p^k


The Attempt at a Solution



E[Y] = sigma(r=1 to k); r * (1-p)^r * p^k * [ (k+r-1)! / k!(r-1)! ]

I have no idea of how to manipulate the factorials...I know that sum of geometric random variables is a negative binomial rv. Since the expectation of a geometric random variable is 1 / p - sum them to r and we get r / p (expectation of NB). However I have to do this with first principles and I'm stumped.

I see a lot of possibilities... p^k / k! is a known series..I even thought about this

for the r * (1-p)^r * p^k part:
= r * q^r * p^k
=r * q^(r-1) * q * p^k
= d/dq(q^r) * q * p^k

Hope this makes some sense...
 
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  • #2
I'm completely stuck and will appreciate any help. As for why does this imply Y is proper, I don't know how to answer this question.
 

FAQ: Expectation of a Negative Binomial RV

What is a Negative Binomial random variable?

A Negative Binomial random variable (NB) is a discrete probability distribution that models the number of successes before a specified number of failures in a sequence of independent and identically distributed Bernoulli trials.

How is the Negative Binomial distribution different from the Binomial distribution?

The Binomial distribution models the number of successes in a fixed number of trials, while the Negative Binomial distribution models the number of trials needed to achieve a fixed number of successes. Additionally, the Binomial distribution has a fixed number of trials, while the Negative Binomial distribution has an infinite number of trials.

What is the formula for calculating the expected value of a Negative Binomial random variable?

The expected value of a Negative Binomial random variable is equal to (p*r)/(1-p), where p is the probability of success in a single trial and r is the number of successes needed.

What is the relationship between the Negative Binomial distribution and the Poisson distribution?

The Negative Binomial distribution can be derived from the Poisson distribution by considering the number of successes in a fixed number of trials. As the number of trials approaches infinity, the Negative Binomial distribution approaches the Poisson distribution with the same mean.

How is the Negative Binomial distribution used in real-world applications?

The Negative Binomial distribution is commonly used in real-world applications such as predicting the number of customer calls in a call center, the number of insurance claims in a given time period, and the number of sales before a certain quota is reached.

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