Convergence in probability distribution

In summary, the conversation is about showing that the sequence of random variables X_n, which follows a geometric distribution with parameter lambda/(n+lambda), converges in distribution to an exponential distribution with parameter 1/lambda. The conversation also discusses the use of laws and the difficulty of understanding this concept. To show this convergence, one needs to understand the definition of convergence in distribution and look at the quantities involved as n approaches infinity.
  • #1
Elekko
17
0

Homework Statement


Let [tex]X_n \in Ge(\lambda/(n+\lambda))[/tex] [tex]\lambda>0.[/tex] (geometric distribution)
Show that [tex]\frac{X_n}{n}[/tex] converges in distribution to [tex]Exp(\frac{1}{\lambda})[/tex]

Homework Equations


I was wondering if some kind of law is required to use here, but I don't know what
Does anyone know how this actually can be shown?
I'm taking a probability class, but the course literature I'm using is does not actually cover examples in this part at all. So it makes me hard to understand this :(
 
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  • #2
Elekko said:

Homework Statement


Let [tex]X_n \in Ge(\lambda/(n+\lambda))[/tex] [tex]\lambda>0.[/tex]
Show that [tex]\frac{X_n}{n}[/tex] converges in distribution to [tex]Exp(\frac{1}{\lambda})[/tex]

Homework Equations


I was wondering if some kind of law is required to use here, but I don't know what
Does anyone know how this actually can be shown?
I'm taking a probability class, but the course literature I'm using is does not actually cover examples in this part at all. So it makes me hard to understand this :(

What is "Ge(.)"?
 
  • #3
Ray Vickson said:
What is "Ge(.)"?

Geometric distribution
 
  • #4
Elekko said:

Homework Statement


Let [tex]X_n \in Ge(\lambda/(n+\lambda))[/tex] [tex]\lambda>0.[/tex] (geometric distribution)
Show that [tex]\frac{X_n}{n}[/tex] converges in distribution to [tex]Exp(\frac{1}{\lambda})[/tex]

Homework Equations


I was wondering if some kind of law is required to use here, but I don't know what
Does anyone know how this actually can be shown?
I'm taking a probability class, but the course literature I'm using is does not actually cover examples in this part at all. So it makes me hard to understand this :(

(1) What is the definition of convergence in distribution? (If you do not know or understand this you cannot profitably proceed further.)

(2) Assuming you have answered (1) correctly, just write down the actual quantities involved (distributions, etc.) and look at what happens when n → ∞. (You should find this to be straightforward; if not, you need to go back to some earlier courses to fill in some missing background.)
 

FAQ: Convergence in probability distribution

What is convergence in probability distribution?

Convergence in probability distribution is a concept in statistics that refers to the idea that as the sample size of a random variable increases, the probability that the values of the random variable approach a certain distribution also increases.

What is the difference between convergence in probability distribution and convergence in distribution?

Convergence in probability distribution refers to the convergence of the probability distribution of a random variable, while convergence in distribution refers to the convergence of the cumulative distribution function of a random variable.

What are the types of convergence in probability distribution?

There are two types of convergence in probability distribution: weak convergence and strong convergence. Weak convergence is also known as convergence in distribution, while strong convergence is also known as convergence in probability.

How is convergence in probability distribution related to the law of large numbers?

The law of large numbers states that as the sample size increases, the sample mean will approach the population mean. Convergence in probability distribution is related to this because as the sample size increases, the probability that the sample mean approaches the population mean also increases.

Why is convergence in probability distribution important in statistics?

Convergence in probability distribution is important in statistics because it allows us to make inferences about a population based on a sample. It also provides a mathematical framework for understanding the behavior of random variables and their distributions as the sample size increases.

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