Convergence in Distribution for Random Vectors

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SUMMARY

The discussion focuses on the convergence in distribution of a sequence of p-dimensional random vectors, Xn, to a normal distribution N_p(μ, Σ). It establishes that Xn converges in distribution to N_p(μ, Σ) if and only if the linear transformation a'Xn converges in distribution to N_1(a'μ, a'Σa). The proof involves the moment-generating function, where E(e^{(a'X_n)t}) is expressed as e^{a'tμ + 0.5t²(a'Σa)}. Participants highlight the need for precision in stating mathematical equalities and the importance of including limits in proofs.

PREREQUISITES
  • Understanding of convergence in distribution for random variables.
  • Familiarity with moment-generating functions (MGFs).
  • Knowledge of multivariate normal distributions, specifically N_p(μ, Σ).
  • Basic linear algebra concepts, particularly linear transformations of random vectors.
NEXT STEPS
  • Study the properties of moment-generating functions in probability theory.
  • Learn about the Central Limit Theorem and its implications for convergence in distribution.
  • Explore the characteristics of multivariate normal distributions, including their applications.
  • Investigate the role of linear transformations in the context of random vectors and their distributions.
USEFUL FOR

Statisticians, mathematicians, and graduate students in probability theory who are working with random vectors and their convergence properties.

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Homework Statement



Let Xn be a sequence of p dimensional random vectors. Show that

Xn converges in distribution to [tex]N_p(\mu,\Sigma)[/tex] iff [tex]a'X_n[/tex] converges in distribution to [tex]N_1(a' \mu, a' \Sigma a).[/tex]

Homework Equations





The Attempt at a Solution



[tex]E(e^{(a'X_n)t} = E(e^{(a't)X_n}) = e^{a't \mu + 0.5t^2(a' \Sigma a)}[/tex]

Hence, {a'Xn} converges [tex]N(a' \mu, a' \Sigma a).[/tex] in distribution.

Is that it?
 
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hey, i can't seem to get this question.
what is the sum of: SIGMA (i=1 to n) of i(i+1)(i+2)... is the answer just infinity or is it some kind of weird expression that i have to find?
 
pstar - you need to start your own thread - intruding into another's isn't appropriate.

To the OP:
You have the outline, but the rough edges need to be smoothed. For example, stating this equality

[tex] E(e^{(a't)X_n}) = E^{a't\mu + 0.5t^2 (a' \Sigma a)}[/tex]

isn't correct - there is a limit involved, correct?

The proof isn't long, and you've got the basic idea, but the details need to be included.
 

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