The Pareto and Exponential Distr. Where is the mistake?

In summary: This is because ln ( X / Xm) = x/(1+x) when x = x_m. If you want to use x instead of x_m, you would have to change the sign of the denominator.
  • #1
MrGandalf
30
0
Hi.

I want to examine the connection between the Pareto Distribution and the Exponential distribution. According to Wikipedia (en.wikipedia.org/wiki/Pareto_distribution) the Pareto distribution's pdf is
[tex]f(x; k, x_m) = k x_m^k x^{-(k + 1)} = \frac{k x_m^k}{x^{(k + 1)}}[/tex]
[itex]x[/itex] is the variable, [itex]x_m[/itex] is the minimum value and [itex]k[/itex] is a positive parameter.

Wiki tells me that the Pareto distribution is linked to the Exponential distribution (which is [itex]f(x;\lambda) = \lambda e^{-\lambda x}[/itex]) by the following:

[tex]f(x; k, x_m) = Exp(\ln(x/x_m); k)[/tex]

I assume there are no mistakes on the Wiki, so I think I'm doing something wrong. Can anyone see any errors in my reasoning? I start with the Exponential distribution and work my way backwards with the specified parameters. (Sorry if you feel I'm repeating myself at the end, just wanted to get the same form as above).

[tex]Exp(\ln(x/x_m); k) \;=\; k e^{-k\ln[x/x_m]} \;=\; k \Big(e^{\ln[x/x_m]}\Big)^{-k}[/tex]
[tex]k\Big(\frac{x}{x_m}\Big)^{-k} \;=\; k\Big(\frac{x_m}{x}\Big)^{k} \;=\; k\frac{x_m^k}{x^k} = k x_m^k x^{-k} \;=\; \frac{k x_m^k}{x^k} \;\not=\; \frac{k x_m^k}{x^{(k + 1)}}[/tex]

Where does the extra [itex]x[/itex] in the denominator come from? I am very confused right now. I can't see anything wrong! I hope someone can enlighten me.
 
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  • #2
Hm, seems right to me, maybe there is indeed an error on wiki. :smile:
 
  • #3
I am digging up an old thread here, sorry.

What you are missing is the fact that f is a density and so you can't just substitute a new variable, you have to do a change of variables accounting for the fact that dx != dy. Otherwise your distribution is "stretched".

The factor 1/x that you are missing comes from d [ ln ( X / Xm) ] / dx.
 

1. What is the Pareto distribution and how is it different from the Exponential distribution?

The Pareto distribution is a statistical model that is used to describe the distribution of wealth or income in a society, where a small number of individuals hold a large proportion of the total wealth. The Exponential distribution, on the other hand, is used to model the time between events in a Poisson process. The main difference between the two is that the Pareto distribution has a power-law tail, meaning that it has a higher probability of extreme events occurring compared to the Exponential distribution.

2. Can the Pareto distribution be used to model other types of data?

Yes, the Pareto distribution can be used to model other types of data, such as the size distribution of cities, the frequency of words in a language, or the popularity of websites. However, it is important to note that the Pareto distribution is not suitable for all types of data and should be used with caution.

3. What is the Pareto principle and how is it related to the Pareto distribution?

The Pareto principle, also known as the 80/20 rule, states that roughly 80% of the effects come from 20% of the causes. This principle is related to the Pareto distribution, as it follows a similar pattern where a small percentage of the population holds a large percentage of the total wealth or income.

4. What is the common mistake made when using the Pareto and Exponential distributions?

The most common mistake when using these distributions is assuming that they are interchangeable. While both distributions have a power-law tail, they have different applications and should not be used interchangeably. Additionally, it is important to carefully choose the appropriate distribution for the specific data being analyzed.

5. How can I determine if my data follows a Pareto or Exponential distribution?

There are various statistical tests that can be used to determine if a dataset follows a Pareto or Exponential distribution. Some common methods include visual inspection of a histogram, the Kolmogorov-Smirnov test, and the maximum likelihood estimation method. It is recommended to consult with a statistician for proper analysis and interpretation of results.

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