# Tag Info

2

So, I had a look at that old version of my notebook and I found it too specific. I copy here part of what was the documentation of my Override.m package. It's more general. (I'll spare you the code required to add options...) Here we go. It's easy to redefine a built-in procedure so that it can call itself without incurring in an infinite recursion. The ...

2

After looking at this a bit more there are closed-form maximum likelihood estimates for the parameters of this doubly-censored shifted exponential distribution. Suppose there are $n$ observations with $n_0$ having the minimum value, $n_2$ having the maximum value, and that we label the $n_1=n-n_0-n_2$ values between the minimum and the maximum values as ...

2

This is an extended comment. I gathered all your data in l and git this CDF l1 = Join @@ l; hl = HistogramList[l1, "Knuth", "CumulativeCount"]; data = Transpose[{MovingAverage[hl[[1]], 2], Rescale@hl[[2]]}]; ListPlot@data This plot strongly suggest that your model isn't a good fit because: 1) There are negative values {-1 ...1} and your model ...

0

2nd Update: Here's brute force method to obtain maximum likelihood estimates and the AIC statistic which you could use in comparing the fit to other distributions. What you have is a doubly censored shifted exponential distribution. The mixed density/probability function is given by (* Define probability function *) f[x_, λ_, β_, γ_] := Piecewise[{ {1 ...

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Apparently not, or at least not without some significant effort to modify the CopulaDistribution function. Looking at the definition of CopulaDistribution and the functions it relies on (in StatisticsCopulaDistributionDump context), all of the kernels are handled individually as special cases, and anything not matching one of these cases is explicitly ...

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I am sure there are more elegant ways to solve this, but one possibility might be the following module. As noted by Alexei Boulbitch it is necessary to transform the arguments of the delta functions as equations to be solved by Solve. The following implementation includes the necessary jacobian factors and allows in principle to start from the complete ...

4

Although @ciao's solution gets you to the answer, I would like to offer perhaps another angle at it. Given a tuple $\{X_1, X_2, \ldots, X_n\}$ that follows a multivariate hypergeometric distribution with parameters $N$, $\{M_1, \ldots, M_n\}$, the tuple $\{X_1, X_2, \sum_{k=3}^n X_k \}$ also follows a multivariate hypergeometric with parameters $N$ and ...

7

This is one of my few gripes re: an otherwise quite nice probability functionality. Sometimes, performance is inexplicably poor. Usually, trivial manual transformation/intervention can get the results desired speedily, e.g., your example cases: ClearAll["Global`*"] dist = MultivariateHypergeometricDistribution[5, ConstantArray[2, 10]]; PDF[dist, ...

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