Confusion about random variables and convergence in probabilty and distribution

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manpreet Tuteehub forum best answer Best Answer 2 years ago

 

I'm studying statistical analysis and there's something fundamental I'm missing about random variables and how they are used in defining convergence in probability or distribution:

In my syllabus (which is in dutch, so the terms i use might be slightly off), when talking about samples, it says that

If we want to study the properties of a random variable XX in a target population, we take a random aselect sample of nn subjects from a collection of nn random variables X1,...XnX1,...Xn that are all mutually (pairwise?) independent and which all have the same distribution, namely that of XX in the target population.

A bit further, discussing convergence, it says

An infinite row of random variables X1,X2,...X1,X2,... on a probability space converges in probability to XX if the folowing is true for each ϵ>0ϵ>0limnP(|XnX|)0limn→∞P(|Xn−X|)≥0

What I don't understand is what XiXi actually means in these two contexts. I read it as follows: In the first part, it is presented as one choice from the population: XiXi is the length of the 

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manpreet 2 years ago

The second paragraph simply says that if limnP(|XnX|ϵ)0limn→∞P(|Xn−X|≥ϵ)→0 then we say XnXXn→X in probability. That's just a definition of what "convergence in probability means".

The first paragraph talks about specific sequences XiXi - namely of those which are independent and identically distributed. You may view those as samples drawn (with replacement) from a fixed population with cumulative distribution function (CDF) DD.

You are correct that such a sequence of samples in general won't converge in probability. In fact, they never do unless the distribution of the XiXi is degenerate, i.e. there's an xx with P(Xi=x)=1P(Xi=x)=1.

They do, however converge in distribution, which is a weaker form of convergence, and means that the cumulative distribution function (CDF) of XnXn converges pointwise to DD, i.e. that limnP(Xnx)=

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