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distributions - Empirical CDF vs CDF - Cross Validated The CDF is a theoretical construct - it is what you would see if you could take infinitely many samples The empirical CDF usually approximates the CDF quite well, especially for large samples (in fact, there are theorems about how quickly it converges to the CDF as the sample size increases)
probability - Find expected value using CDF - Cross Validated @styfle - because that's what a PDF is, whenever the CDF is continuous and differentiable You can see this by looking at how you have defined your CDF Differentiating an integral just gives you the integrand when the upper limit is the subject of the differentiation
estimation - What is the proper way to estimate the CDF for a . . . My initial thought was 'I don't think there is an answer to this question ' Similar to what is written in one of the replies The idea of a confidence interval about some smooth curve, and then getting narrower as n increases, seems like a cool idea, though
Distribution function terminology (PDF, CDF, PMF, etc. ) Distribution Function Cumulative Distribution Function (CDF) Probability Distribution Function Probability Density Function Probability Mass Function (PMF) (1) Distribution Function == CDF I e they are the same Am I right? (2) Which is called PDF: Probability Distribution Function or Probability Density Function?