Law of Large Numbers - Rate of convergence

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Discussion Overview

The discussion revolves around the rate of convergence of the law of large numbers, specifically questioning whether the convergence can be expressed in terms of a power of n, and if so, what the exponent might be. The scope includes theoretical aspects of probability and convergence rates.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • One participant asks if the convergence of the sum can be expressed as n^\alpha for some α in the real numbers.
  • Another participant notes that the rate of convergence is highly dependent on the distribution functions of the random variables involved.
  • A third participant references existing literature that provides results related to the topic but does not include proofs.
  • A fourth participant mentions theorems known as "Laws of the iterated logarithm" that may provide answers and suggests consulting probability texts for further discussions.

Areas of Agreement / Disagreement

Participants express differing views on the rate of convergence, with no consensus on the value of α or the implications of different distribution functions.

Contextual Notes

The discussion does not resolve the mathematical steps or assumptions necessary to determine the rate of convergence, and the dependence on specific distribution functions remains unresolved.

Apteronotus
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What is the rate of convergence of the law of large numbers?

ex.
if
[tex] lim_{n \rightarrow \infty} \frac{1}{n} \sum Z_n = \mu[/tex]

1. can we say that the sum converges to [tex]\mu[/tex] as [tex]n^\alpha[/tex] for some [tex]\alpha\in \Re[/tex]?

2. If so, what is the value of [tex]\alpha[/tex]?

Thanks,
 
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It depends very much on the distribution functions of the random variables involved.
 
Theorems generically known as "Laws of the iterated logarithm" will give some answers. You can find discussions in probability texts (Chung, for example). A very good discussion is in the book "Approximation Theorems of Mathematical Statistics".
 

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