A limit in probability (possibly dominated or monotone convergence theorem)

In summary: You can directly use ##a## in your argument.In summary, we are given an equation that implies an integral converges to 1. By using a substitution and rearranging, we can rewrite the integral as a limit of an increasing sequence of integrals. We can then use the Monotone Convergence Theorem (or the Dominated Convergence Theorem) to show that the limit exists and is equal to 1.
  • #1
Gregg
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Homework Statement



## \sqrt{2\pi} a \exp({a^2 \over 2} ) P( \xi \geq a) \to 1 ##

## \xi \text{ ~ } N(0,1) ##

Homework Equations

This implies

##\sqrt{2\pi} a \exp({a^2 \over 2} ) \int_a^\infty a \exp({a^2\over 2}) \exp (-{x^2 \over2}) dx \to 1 ##

The Attempt at a Solution



The integral doesn't seem to converge to anything obvious to me. I think that as a grows,the integrand grows large but the interval in which we integrate over is getting smaller.

Try and use a substitution.

##x^2 = a^2 + 2y ## and do some rearranging.

## \int_0^\infty y \frac{\exp({-y\over 2})}{\sqrt{1+2y/a^2}} dy ##

Monotone convergence theorem

we have that ## y \frac{\exp({-y\over 2})}{\sqrt{1+2y/a^2}} \nearrow y \exp({-y\over 2}) ##

So

## \mu (f_a) \nearrow \mu(f) < \infty ##

##\mu(f) = 1 ##

Is this right? Do I need to show that ##\mu(f_n) \nearrow \mu(f) ## ? also the subscript n doesn't appear in the integrand, I couldn't use a could I, and a seems to be continuous and so it isn't a sequence of functions?
 
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  • #2


Your approach is correct. You can use the Monotone Convergence Theorem to show that the integral converges to a finite value as ##a \to \infty##. You can also use the Dominated Convergence Theorem, since the integrand is bounded by ##y e^{-y/2}##, which is integrable on the given interval.

You are correct that the subscript ##n## is not needed in the integrand, since it is a continuous function of ##a##.
 

Related to A limit in probability (possibly dominated or monotone convergence theorem)

What is a limit in probability?

A limit in probability refers to the concept of the probability of an event occurring as the number of trials or experiments approaches infinity. It is used to describe the long-term behavior or trend of a sequence of random events.

What is the dominated convergence theorem?

The dominated convergence theorem is a mathematical theorem that states that if a sequence of random variables is bounded by another sequence of random variables that converge to 0, then the limit of the original sequence also converges to 0. It is used to simplify the calculation of limits in probability.

What is the monotone convergence theorem?

The monotone convergence theorem states that if a sequence of random variables is monotone, either increasing or decreasing, and bounded, then the limit of the sequence exists and is equal to the limit of the integral of the sequence. It is used to prove the existence of limits in probability.

How are the dominated and monotone convergence theorems related to limits in probability?

The dominated and monotone convergence theorems are both used to prove the existence and simplify the calculation of limits in probability. They provide necessary conditions for the convergence of a sequence of random variables and help to determine the value of the limit.

What are some applications of limits in probability?

Limits in probability have various applications in fields such as statistics, economics, and physics. They are used to model and predict the behavior of complex systems, analyze data, and make decisions based on uncertain events. Some specific examples include estimating the probability of a stock market crash, predicting the likelihood of a natural disaster, and determining the expected value of a random variable.

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