Modulo sum of random variables

In summary, the conversation discusses the theorem that states if X is uniformly distributed over [0,a) and Y is independent, then X + Y (mod a) is also uniformly distributed over [0,a) regardless of the distribution of Y. The theorem is proven using examples and references to a paper that supports it. A question is raised about the applicability of the theorem in the case that Y is defined over [0,a], but it is noted that the theorem still holds due to the distribution of (X + Y) mod a and (X + (Y mod a)) mod a being the same.
  • #1
areslagae
11
0
If X is uniformly distributed over [0,a), and Y is independent, then X + Y (mod a) is uniformly distributed over [0,a), independent of the distribution of Y.

Can anyone point me to a statistics text that shows this?

Thanks,
 
Physics news on Phys.org
  • #2
areslagae said:
If X is uniformly distributed over [0,a), and Y is independent, then X + Y (mod a) is uniformly distributed over [0,a), independent of the distribution of Y.

Can anyone point me to a statistics text that shows this?

Thanks,

Consider any possible value from Y. Since X and Y are independent, X + that value is uniformly distributed mod a. Now since this is true for any value, it is true for any combination of values.
 
  • #3
I don't know of a text, but the proof is simple enough. Let Y=y, then X+y (mod a) is uniformly distributed, since X is independent of Y. Therefore the theorem holds irrespective of the distribution of Y.
 
  • #4
Thanks for both your replies!

At first, me (and my collegues) found this result somewhat counter-intuitive. It seems that you do not, but you most likely you have a deeper intuition.

Meanwhile, I also found the following paper which is interesting in this context:
The Distribution Functions of Random Variables in Arithmetic Domains Modulo a
P. Scheinok
http://www.jstor.org/stable/2310973

It seems that the theorem from my first post follows from 3.3, with g_Y()=1/a.

Thanks,
 
Last edited by a moderator:
  • #5
Hi all,

I have a quick additional question.

A colleague pointed out to me that the cited paper only proves the theorem from my first post in the case that Y is defined over [0,a].

However, the random variables (X + Y) mod a and (X + (Y mod a)) mod a have the same distribution, so the original theorem should hold, no?

Thanks,
 

Related to Modulo sum of random variables

1. What is the "Modulo sum of random variables"?

The modulo sum of random variables is a statistical concept that involves taking the sum of random variables and then finding the remainder when divided by a specified number, known as the modulus. It is often used in situations where the data has a cyclical or repetitive nature, such as in time series analysis.

2. How is the modulo sum of random variables calculated?

The modulo sum of random variables is calculated by first finding the sum of the random variables, and then dividing this sum by the modulus. The remainder of this division is the modulo sum of the random variables.

3. What is the significance of using the modulo sum in statistical analysis?

The modulo sum is useful in statistical analysis because it allows for the identification of patterns and cyclical trends in data. It can also help to reduce the impact of outliers and extreme values on the overall analysis.

4. Can the modulo sum be applied to any type of random variables?

Yes, the modulo sum can be applied to any type of random variables, including discrete and continuous variables. However, it is most commonly used with discrete variables that have a finite range of values, such as integers.

5. Are there any limitations to using the modulo sum in statistical analysis?

While the modulo sum can be a useful tool, it is important to note that it may not be appropriate for all types of data. It may not accurately represent the underlying distribution of the data and can be influenced by the choice of modulus. Additionally, the use of the modulo sum may result in loss of information and may not be suitable for more complex statistical analyses.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
30
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
582
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
553
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
728
  • Set Theory, Logic, Probability, Statistics
Replies
9
Views
637
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
978
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
705
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
Back
Top