Calculate covariance matrix of two given numbers of events

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The discussion focuses on calculating the covariance matrix for two variables, N1 and N2, derived from event distributions defined by specific functions. The user initially struggles with understanding the covariance matrix calculation as outlined in a referenced paper. After attempting various methods, they successfully derive the covariance matrix using error propagation techniques, specifically for large N values. They express a need for resources or literature that provide proofs or further explanations of their findings. The user also seeks assistance with formatting issues related to LaTeX expressions in their posts.
Brais
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Hi, I am trying to follow this paper: (arXiv link).
On page 18, Appendix A.1, the authors calculate a covariance matrix for two variables in a way I cannot understand.

Homework Statement


Variables N_1[\itex] <br /> and N_2[\itex], distributed on &lt;br /&gt; y \in [0, 1][\itex] as follows:&amp;lt;br /&amp;gt; f_1(y&amp;amp;amp;lt;y_0) = 0; f_1(y&amp;amp;amp;gt;= y_0) = \frac{1}{1-y_0}&amp;lt;br /&amp;gt; f_2 = 1[\itex]&amp;amp;lt;br /&amp;amp;gt; Define: N_{&amp;amp;amp;amp;lt;} = [\itex]amount of events falling below y_0[\itex], N_{&amp;amp;amp;amp;amp;amp;gt;}[\itex] analogously...&amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;lt;h2&amp;amp;amp;amp;amp;gt;Homework Equations&amp;amp;amp;amp;amp;lt;/h2&amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;gt; Then, we can calculate:&amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;gt; N_2 = \frac{1}{y_0}N_{&amp;amp;amp;amp;amp;amp;amp;lt;}[\itex]&amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;gt; N_1 = -\frac{1-y_0}{y_0}N_{&amp;amp;amp;amp;amp;amp;amp;amp;lt;} + N_{&amp;amp;amp;amp;amp;amp;amp;amp;gt;}[\itex]&amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;gt; Covariance of two variables: Cov(a,b) = &amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;(a·b)^2&amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; - &amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;a·b&amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;^2[\itex]&amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;lt;h2&amp;amp;amp;amp;amp;amp;amp;amp;gt;The Attempt at a Solution&amp;amp;amp;amp;amp;amp;amp;amp;lt;/h2&amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;gt; After calculating the two previous results myself (N1 and N2), I tried to calculate the covariance matrix V (please see pdf). I tried to do it from the definition of covariance, but I didn&amp;amp;amp;amp;amp;amp;amp;amp;amp;#039;t get anywhere (or to different results). So I decided to guess if the authors were doing the usual error propagation from N_1, N_2[\itex] as a function of N_{&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;}, N_{&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;}[\itex].&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; Considering the usual in physics for N large, \sigma(N_k) = \sqrt(N_k), k \in {&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;, &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;}[\itex], and then, for instance \sigma^2(N_2) =\sum_k \frac{\partial N_2}{\partial N_k} \sigma(N_k) = \frac{1}{y_0 ^2} N_{&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;}[\itex] (the right result).&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; However, I cannot extend this to the off-diagonal terms.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; Could somebody please help me? Thanks!&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;br /&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; EDIT: I cannot see the latex expressions, and instead I see all my itex \itex ! How can I solve this? Thanks!
 
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Right...
after some trial and error, I got to an expression for the right solution of the covariance matrix:

Given the (2x2) covariance matrix V, and the variables:

N1 = a1/c
N2 = -(1-c)a1/c + a2

I calculated V(m,n) = sum(i = 1 to 2) (dNm/da_i)(dNn/da_i) Error^2(Ni),
where Error^2(Ni) = Ni, and m, n are either 1 or 2.

This works and I get the right solution. Also, the expression makes sense... However, I couldn't find this in a book on statistics. Could somebody point me in a good direction? Do you know of any book/page were I can see the proof of this?

Thank you! And sorry for the wrong latex formulae in the previous post, but I don't know how to fix it. I counted the number of itex \itex and it is right :S
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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