Derivatives of Cauchy Distribution

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Discussion Overview

The discussion revolves around the derivatives of the log-likelihood function (LLF) for the Cauchy distribution, particularly in the context of optimizing parameters using Newton's method. Participants are examining potential mistakes in the derivatives that could affect the estimation of the scale parameter.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents the log-likelihood function and its first and second derivatives, expressing confusion over the optimization results for the scale parameter.
  • Another participant emphasizes the importance of correctly applying the derivative rules, specifically pointing out a mistake in the treatment of sums in derivatives.
  • A later reply clarifies that the data set is known and that the focus is on estimating the parameters m and s, while also addressing the differentiation of a fraction.
  • Another participant suggests that the initial derivatives might be incorrect based on the way they were presented, indicating that this could lead to issues in subsequent calculations.

Areas of Agreement / Disagreement

Participants express differing views on the correctness of the derivatives presented, with some agreeing on the need for careful differentiation while others question the initial formulations. The discussion remains unresolved regarding the accuracy of the derivatives and their implications for the optimization process.

Contextual Notes

There are indications of potential typographical errors in the derivatives as presented, and the discussion highlights the complexity of correctly applying derivative rules in the context of sums and fractions. Specific assumptions about the data set and the parameters being estimated are also noted.

riemann01
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Hi guys,

I would like to ask you where you spot the mistake in the derivatives of the loglikelihood function of the cauchy distribution, as I am breaking my head :( I apply this to a Newton optimization procedure and got correct m, but wrong scale parameter s. Thanks!

[tex] LLF = -n\ln(pi)+n\ln(s)-\sum(\ln(s^2+(x-m)^2)),[/tex]

First Derivatives:
[tex] \frac {dL} {dm} = 2\sum(x-m) / \sum(s^2+(x-m)^2)[/tex]
[tex] \frac {dL} {ds} = n/s - 2\sum(s) / \sum(s^2+(x-m)^2)[/tex]

Second Derivatives:
[tex] \frac {d^2L} {dm^2} = (-2n(\sum(s^2+(x-m)^2)))+4\sum(x-m)^2)/(\sum(s^2+(x-m)^2))^2[/tex]
[tex] \frac {d^2L} {ds^2} =-n/s^2-2\sum(-s^2+(x-m)^2)/(\sum(s^2+(x-m)^2))^2[/tex]
[tex] \frac {d^2L} {dmds} =-4\sum(s(x-m))/(\sum(s^2+(x-m)^2))^2[/tex]
 
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I would point out that if you have a term like this:

[tex] \sum_{i=1}^n {\left(\ln (10 + x_i^2)\right)}[/tex]

then the first derivative is

[tex] \sum_{i=1}^n {\frac{2x_i}{10+x_i^2}[/tex]

and not

[tex] \frac{\sum_{i=1}^n {2x_i}}{\sum_{i=1}^n (10+x_i^2)}[/tex]
 
Thanks for the reply statdad, you are correct but x is known and is a data set, what we are looking is m and s, for instance:

[tex] \sum \frac{1}{10+(x-m)^2}, -\sum \frac{-2(x-m)}{(10+(x-m)^2)^2} [/tex]

The first - comes from the formula of fraction differentiation and the second minus from differentiating the (x-m).
 
Last edited:
I realize full well what this is about: my point came from an apparent type in your first post. You essentially wrote

[tex] \frac{dL}{dm} = \frac{2\sum{(x-m)}}{\sum{(10+(x-m)^2)^2}}[/tex]

I made a poor choice in using [itex]x_i[/itex] in my example - I was merely trying to hint that you can't distribute the sum to numerator and denominator. My point was this: if you are starting with the first derivatives as written, the fact that they (seem to be, in the typing of your first post) incorrect could be the cause of your subsequent problem.
 

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