What Is E{x|x+y+z=1} for Independent Standard Normal Variables?

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In summary, the distributions of x, y, and z being standard normal with mean 0 and standard deviation 1, and being statistically independent, allows for the equality of E{x|x+y+z=1}, E{y|x+y+z=1}, and E{z|x+y+z=1}. This is due to the symmetry of x, y, and z, meaning that permuting the letters x,y,z in the conditional expected values does not change their values. However, if the variables were not independent, this symmetry would not hold.
  • #1
purplebird
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Given x,y and z are standard normal distributions with mean 0 and standard deviation 1. x,y and z are also statistically independent.

Find E{x|x+y+z=1}.
 
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  • #2
symmetry of x, y, and z. For example, interchanging x and y in E[x| x+y+z=1] to get E[y|y+x+z = 1] doesn't change its value.
 
  • #3
Adeimantus said:
symmetry of x, y, and z. For example, interchanging x and y in E[x| x+y+z=1] to get E[y|y+x+z = 1] doesn't change its value.

I am not able to understand what you are referring to. Could you please explain. Thanks.
 
  • #4
Sure thing... If I understand the problem correctly, it doesn't really matter what the exact distributions of x, y, and z are. It only matters that they are identically distributed. Also, note that the condition x+y+z = 1 is unchanged by permuting the letters x,y,z. This, together with their being identically distributed means that

E{x|x+y+z=1} = E{y|x+y+z=1} = E{z|x+y+z=1}


Also, think about what E{x+y+z|x+y+z=1} would be, and remember the additive property of the expected value.

edit: think simple. no hard integrals needed, which was the first thing that came to my mind when I read the problem.
 
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  • #5
I left something out...it is also important that x,y,z are statistically independent. If, for example, x and y were statistically dependent, but x and z were not, then that would create an asymmetry and you could no longer conclude that the expected values of x, y, and z were equal.
 

FAQ: What Is E{x|x+y+z=1} for Independent Standard Normal Variables?

1. What is the conditional expectation problem?

The conditional expectation problem is a statistical concept that involves finding the expected value of a random variable given certain conditions or information. It is used to predict the value of a variable based on known values of other related variables.

2. How does conditional expectation differ from regular expectation?

Regular expectation, also known as unconditional expectation, calculates the average value of a random variable without considering any additional information or conditions. Conditional expectation, on the other hand, takes into account specific conditions or known values of other variables to predict the value of the variable of interest.

3. What is the formula for calculating conditional expectation?

The formula for calculating conditional expectation is E(X|Y) = ∑xP(X=x|Y=y), where E(X|Y) represents the conditional expectation of X given Y, ∑x represents the sum of all possible values of X, P(X=x|Y=y) represents the probability of X=x given Y=y.

4. How is conditional expectation used in real-life applications?

Conditional expectation is used in various fields, including finance, economics, and engineering, to make predictions and analyze data. It can be used to forecast stock prices, estimate insurance premiums, and model complex systems.

5. What are the limitations of conditional expectation?

Conditional expectation assumes that there is a linear relationship between the variables, which may not always be the case. It also requires a large amount of data and may not be accurate if the underlying assumptions are not met. Additionally, it cannot account for unexpected or unknown factors that may affect the outcome.

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