1st order or 2nd order distribution

In summary, the speaker is fitting a distribution to a sample of 3,000 journey starting times and is considering whether to use a 1st or 2nd order non-parametric distribution. The speaker has already fitted a parametric distribution, but is uncertain about the fit and is seeking advice on which distribution to use.
  • #1
bradyj7
122
0
Hi,

I'm fitting a distribution to the starting times of the first car journey in the day. I have a sample of 3,000 journey starting times. I am assuming that this sample represents the population well.

I'm fitting a non parametric distribution.

But my question is, should I fit a 1st order or 2nd order distribution?

It should be second order correct? because there is uncertainty about the parameters?

Thanks
 
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  • #2
Hey bradyj7.

What are you saying doesn't make sense because when you fit something to something else, that something else has existing form that you fit to.

What exactly are you trying to fit to?
 
  • #3
Hello Chiro,

Well I originally fitted a parametric distribution to the data and a Johnson unbounded distribution was the best fit using an SIC score.

Here it is:

https://dl.dropbox.com/u/54057365/All/g1.JPG

But I did not think that the fit was good enough, so I fitted an Ogive distribution, a non parametric 1st order distribution.

Here it is
https://dl.dropbox.com/u/54057365/All/g2.JPG


However because I only have a sample of journey start times, I am thinking that there is uncertainty about the non-parametric distribution, so perhaps I should fit a non parametric 2nd order distribution. The red line is the uncertainty distribution.

here it is:
https://dl.dropbox.com/u/54057365/All/g3.JPG



What do you think?

Thanks
 

1. What is the difference between 1st order and 2nd order distribution?

1st order distribution refers to a distribution where the probability of an event occurring is independent of any previous events. 2nd order distribution, on the other hand, takes into account the outcome of previous events when calculating the probability of an event occurring.

2. How are 1st order and 2nd order distribution used in statistics?

1st order distribution is commonly used to model independent events such as rolling a die or flipping a coin. 2nd order distribution is used to model dependent events, such as weather patterns or stock market fluctuations.

3. What is an example of a 1st order distribution?

An example of a 1st order distribution would be flipping a fair coin. Each time the coin is flipped, the probability of getting heads or tails is always 1/2, regardless of previous outcomes.

4. Can a 1st order distribution be converted to a 2nd order distribution?

Yes, a 1st order distribution can be converted to a 2nd order distribution by taking into account the outcomes of previous events. This can be done through techniques such as Markov chains or Bayesian statistics.

5. Which type of distribution is more commonly used in real-world applications?

Both 1st order and 2nd order distributions have their own uses and applications. However, 2nd order distributions are more commonly used in real-world applications as they can better model and predict complex systems and phenomena that involve dependent events.

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