MHB Solving Minimization Problem Involving Variance & Covariance

AI Thread Summary
The discussion centers on minimizing the expression E[(X-b)^2], confirming that the optimal value for b is indeed E[X]. The proof involves differentiating the expression and setting it to zero, which is validated by participants. A subsequent question arises regarding the minimization of E[(Y-aX-b)^2], where Y is a random variable, leading to confusion about the roles of variances and covariances in the optimization process. Clarifications indicate that E(X) and Var(X) are constants, while Cov(Y, aX+b) is a variable in this context, complicating the minimization. Understanding these distinctions is crucial for correctly solving the problem.
OhMyMarkov
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Hello Everyone!

What $b$ minimizes $E[(X-b)^2]$ where $b$ is some constant, isn't it $b=E[X]$? Is it right to go about the proof as follows:

$E[(X-b)^2] = E[(X^2+b^2-2bX)] = E[X^2] + E[b^2]-2bE[X]$, but $E = b$, we differentiate with respect to $b$ and set to zero, we obtain that $b=E[X]$. Is this proof correct? I was thinking it was until I got this problem:

What $Y$ minimizes $E[(Y-aX-b)^2]$? The given expression contains variances and covariances, but all I get was $Y=aE[X]+b$.

What am I doing wrong here?

Any help is appreciated! :D
 
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OhMyMarkov said:
Hello Everyone!

What $b$ minimizes $E[(X-b)^2]$ where $b$ is some constant, isn't it $b=E[X]$? Is it right to go about the proof as follows:

$E[(X-b)^2] = E[(X^2+b^2-2bX)] = E[X^2] + E[b^2]-2bE[X]$, but $E = b$, we differentiate with respect to $b$ and set to zero, we obtain that $b=E[X]$. Is this proof correct?


Correct


I was thinking it was until I got this problem:

What $Y$ minimizes $E[(Y-aX-b)^2]$? The given expression contains variances and covariances, but all I get was $Y=aE[X]+b$.

What am I doing wrong here?

Any help is appreciated! :D

The problem with this second question is that with normal naming conventions \(Y\) is a random variable not a constant, if it were a constant what you get would be correct. If it is a RV then it leaves you with a minimisation problem where the variables are \( \overline{Y}\), \(Var(Y)\), \( Covar(Y,aX+b)\). This is a constrained minimisation problem as \( | Covar(Y,aX+b)| \le \sqrt{Var(Y)Var(aV+b)}\)

CB
 
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Thank you CaptainBlack!

But, you mentioned [FONT=MathJax_Math-italic]E[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main]][FONT=MathJax_Main],[FONT=MathJax_Math-italic]V[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]r[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main]][FONT=MathJax_Main],[FONT=MathJax_Math-italic]C[FONT=MathJax_Math-italic]o[FONT=MathJax_Math-italic]v[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main],[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]+[FONT=MathJax_Math-italic]b[FONT=MathJax_Main]] , what about [FONT=MathJax_Math-italic]E[FONT=MathJax_Main][[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]][FONT=MathJax_Main],[FONT=MathJax_Math-italic]V[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]r[FONT=MathJax_Main][[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]] , can we sub them for [FONT=MathJax_Math-italic]C[FONT=MathJax_Math-italic]o[FONT=MathJax_Math-italic]v[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main],[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]+[FONT=MathJax_Math-italic]b[FONT=MathJax_Main]] ? Or are the variables intentionally used in this fashion so that the hand calculation becomes easier?
 
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OhMyMarkov said:
Thank you CaptainBlack!

But, you mentioned [FONT=MathJax_Math-italic]E[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main]][FONT=MathJax_Main],[FONT=MathJax_Math-italic]V[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]r[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main]][FONT=MathJax_Main],[FONT=MathJax_Math-italic]C[FONT=MathJax_Math-italic]o[FONT=MathJax_Math-italic]v[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main],[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]+[FONT=MathJax_Math-italic]b[FONT=MathJax_Main]] , what about [FONT=MathJax_Math-italic]E[FONT=MathJax_Main][[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]][FONT=MathJax_Main],[FONT=MathJax_Math-italic]V[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]r[FONT=MathJax_Main][[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]] , can we sub them for [FONT=MathJax_Math-italic]C[FONT=MathJax_Math-italic]o[FONT=MathJax_Math-italic]v[FONT=MathJax_Main][[FONT=MathJax_Math-italic]Y[FONT=MathJax_Main],[FONT=MathJax_Math-italic]a[FONT=MathJax_Math-italic]X[FONT=MathJax_Main]+[FONT=MathJax_Math-italic]b[FONT=MathJax_Main]] ? Or are the variables intentionally used in this fashion so that the hand calculation becomes easier?

\(E(X)\) and \(Var(X)\) are constants for this problem, while \(u=Covar(Y,aX+b)\) is one of the variable in the optimisation problem.

CB
 
Hello, I'm joining this forum to ask two questions which have nagged me for some time. They both are presumed obvious, yet don't make sense to me. Nobody will explain their positions, which is...uh...aka science. I also have a thread for the other question. But this one involves probability, known as the Monty Hall Problem. Please see any number of YouTube videos on this for an explanation, I'll leave it to them to explain it. I question the predicate of all those who answer this...
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