Derivatives of Cauchy Distribution

In summary, the conversation discusses the derivatives of the loglikelihood function of the Cauchy distribution and how they are used in a Newton optimization procedure. The first and second derivatives are shown, and a mistake in the first derivative is pointed out. The conversation then elaborates on the mistake and how it may be causing problems in the procedure.
  • #1
riemann01
2
0
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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  • #2
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]
 
  • #3
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:
  • #4
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.
 

Related to Derivatives of Cauchy Distribution

1. What is the Cauchy distribution?

The Cauchy distribution is a continuous probability distribution that is used to model random variables with heavy tails. It is named after the mathematician Augustin-Louis Cauchy and is also known as the Lorentz distribution.

2. What are the main properties of the Cauchy distribution?

The Cauchy distribution has several important properties, including a symmetric shape, infinite support, and a single parameter, the location parameter, which determines the location of the peak of the distribution. It also has heavy tails, meaning that the probability of extreme values is higher compared to other distributions.

3. How are derivatives of Cauchy distributions used in statistics?

The derivatives of Cauchy distributions are used to calculate the probability density function (PDF) and cumulative distribution function (CDF) of the distribution. These are important tools in statistical analysis, as they allow us to make predictions about the likelihood of certain outcomes based on the distribution of the data.

4. What is the relationship between the Cauchy distribution and the standard normal distribution?

The Cauchy distribution and the standard normal distribution have some similarities, such as both having a symmetric shape and infinite support. However, the Cauchy distribution has heavier tails compared to the normal distribution, meaning it has a higher probability of extreme values. Additionally, the Cauchy distribution has a single parameter, while the normal distribution has two (mean and standard deviation).

5. What are some real-world applications of the Cauchy distribution?

The Cauchy distribution is commonly used in physics, particularly in the study of quantum mechanics and particle physics. It is also used in economics and finance to model extreme events, such as stock market crashes. Additionally, it has applications in fields such as psychology, where it can be used to model decision-making processes and biases.

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