User Portlet User Portlet

Discussions
You do get a peak of the density of points at the origin. The density of points for a bivariate normal distribution with mean vector (0,0) does have a peak at (0,0). Hans introduced the distribution of the "distance" from the origin which doesn't...
I won't confess to a "whim" but anything from 31 and on seems to provide a smooth enough prediction (a prediction much smoother than the observed data). Also using an odd number gets one the appropriate associated time (the middle of the time...
It would be nice if the following worked: dist = TransformedDistribution[x1 + x2, {x1 \[Distributed] NormalDistribution[\[Mu], \[Sigma]], x2 \[Distributed] NakagamiDistribution[m, \[CapitalOmega]]}, Assumptions -> {m >= 1/2,...
The documentation for `FindMaximum` states in the first line: "searches for a local maximum in f, starting from an automatically selected point." So you can't necessarily get there from here. However, `Maximize` guarantees a global maximum for...
Same thing as my previous answer. You are attempting to fit an overparameterized model. You've included the intercept and two values for the nominal variable when there are only 2 of those parameters that are estimable. This issue should be...
No. Read the online reference of Discrete Multivariate Distributions under Scope. You still need dx.
I think the main issue is that both models are overparameterized for the available data (and secondarily better starting values are needed). A plot of the data shows just slight curvature over values of $t$ for both datasets suggesting that maybe...
Karl Pearson had a lot of methods. Do you mean what is described in > Pearson, K. (1901), On Lines and Planes of Closest Fit to Systems of > Points in Space
Cross-posted at [Mathematica StackExchange][1]. [1]: https://mathematica.stackexchange.com/questions/215688/improving-the-fitting-of-model-by-nminimize
Thanks! That makes much more sense. (And I do realize that despite the serial correlation, the parameter estimates from `NonlinearModelFit` can be not too bad; just their standard errors are more likely to be underestimated when ignoring the serial...