Biased distribution on semi-infinite axis

In summary, the conversation discusses a distribution that cannot have a mean of 0 and has a bell-shaped look with a mean of 5 and a faster drop-off towards positive infinity. The person is looking for standard models for curve-fitting and mentions the Maxwell distribution as a potential option. However, they are unsure if it will work due to the small tail towards the left. They mention trying other versions such as x^3 and x^4.
  • #1
mikeph
1,235
18
Hi

Usually I'm used to dealing with symmetric distributions with mean at 0, but one has come up that cannot take this form because 0 is impossible to attain. Instead, this function seems to have a bell-shaped look, although the mean is around 5 and the tail towards 0 drops off a little bit faster than towards the positive infinity. Given that I know this distribution has a minimum above 0, are there any standard models that I can use for curve-fitting?

The Maxwell distribution looks similar, I think this is (x^2)*exp(-x^2) plus normalizing factors, but this still goes to zero- I suppose I can shift x but it's not very "tidy". I'm going to test it nevertheless, but I'd like to know if there are any other models I can compare it to for perspective?

Thanks
 
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  • #2
Any ideas? It's got an incredibly small tail going leftwards...100,000 samples found a minimum of about 4.7, but I know it goes down below 3 at least.

Here's the shape

2cfce8n.jpg



Maxwell's version won't work because I think the best fit will find a zero at about 4.5. I'm trying x^3 and x^4 versions but I'm not sure if they're going to work either.
 

1. What is a biased distribution on a semi-infinite axis?

A biased distribution on a semi-infinite axis is a type of probability distribution where the values are not equally likely to occur. This means that some values are more likely to occur than others, resulting in a skewed distribution.

2. How is a biased distribution different from a normal distribution?

A normal distribution, also known as a bell curve, is symmetrical with the majority of values falling around the mean. A biased distribution, on the other hand, is not symmetrical and often has a longer tail on one side, indicating a higher frequency of certain values.

3. What causes a biased distribution on a semi-infinite axis?

A biased distribution can be caused by a variety of factors, such as a non-random sampling process, underlying patterns or relationships between variables, or the influence of external factors. It can also occur naturally in certain types of data, such as income or population data.

4. How is a biased distribution measured?

A biased distribution can be measured using various statistical measures, such as skewness and kurtosis. Skewness measures the asymmetry of the distribution, while kurtosis measures the peakedness of the distribution. A high skewness or kurtosis value indicates a more biased distribution.

5. What are the implications of a biased distribution on a semi-infinite axis?

A biased distribution can have significant implications in data analysis and decision making. It can lead to incorrect conclusions and biased predictions, as well as affect the accuracy of statistical models. It is important to identify and address biased distributions in order to make accurate and unbiased interpretations of data.

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