How to plot confidence bounds for a theoretical cumulative distribution function?

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I understand how to plot upper and lower confidence bounds for an experimental cumulative distribution function using the ecdf function.
But how to plot upper and lower confidence bounds for a theoretical cumulative distribution like for example the Theoretical CDF in the plot shown below? (copied from: https://www.mathworks.com/help/stats/cdfplot.html)
  6 comentarios
Eric-Jan Scharlee
Eric-Jan Scharlee el 6 de Ag. de 2021
Thank you, Paul, the paramci() function (when applied to the probability distribution calculated with fitdist()) provides the confindence intervals for the distribution parameters. Can these be used to calculate the entire upper and lower confidence bounds "around" the probability distribution? Is there a generic formula for that or is the formula dependent on the type of probability distribution?
I agree with your question to @Jeff Miller, thanks for explaining it like that, because indeed, I want to be able to calculate the upper and lower confidence bounds for various types of probability distributions, dependent on what the outcome of the hypothesis tests are in terms of which probability distribution is fitting the observed data in the best way (highest p-value).
Paul
Paul el 6 de Ag. de 2021
I don't know if there is a way to do what you want; but I'm far from an expert on such things. Having said that, my intuition is that fitting a distribution is an exercise in estimating the parameters of the distribution, and that's why fitdist only returns the CI's around the parameters, i.e., it's only those parameters that are being estimated. In contrast, the ecdf() function is estimating something at each value in the xdata, so there it seems reasonable to come up with a CI around each estimate, which is what the dotted curves are in that plot. I'll be interested to see other answers to your question.

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Jeff Miller
Jeff Miller el 6 de Ag. de 2021
For a given X value, the theoretical cumulative probability is p = F(X). Suppose you have a sample of N observations and you let k be the number of observations <= X. k is (by definition) binomial(N,p) with the known N and that theoretical p. Using that binomial distribution, you can get upper and lower confidence limits on the observed k (e.g., with a normal approximation to the binomial). Then divide those upper and lower lilmits on k by N and you will have upper and lower confidence limits on p for that X value.
  4 comentarios
Paul
Paul el 6 de Ag. de 2021
Based on the comment chain in the question, it sounds like @Eric-Jan Scharlee wants to fit a specified CDF (e.g,. Weibull) to some data, and then show a confidence interval around the fitted distribution. Is that the process you're describing?
Jeff Miller
Jeff Miller el 6 de Ag. de 2021
Thanks for the clarification, @Paul. I misunderstood the original question as pertaining to a known theoretical distribution (i.e., with known parameter values). No, the process I am describing does not apply to fitted distributions.
@Eric-Jan Scharlee As Paul says, CIs around parameter estimates are standard, but CIs around a fitted CDF are not. I am not even sure how that would be defined. I suppose you could generate a range of CDFs with different combinations of parameter values within the parameter CIs and take the extremes of those (at each X) as some kind of theoretical CI, but that's really ad hoc. To me, it seems better to focus on the CIs from the ecdf.

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