I am trying to test if the q-Weibull distribution is better at describing the behavior of certain data than other distributions. Since I am from a biological sciences background, math and coding are not my stronger suits, and I have been struggling a little bit with implementing such analysis. I am basing my analysis on a paper on the subject of estimating the parameters of q-Weibull distributions. The paper states that to estimate the parameters I have to equate the following equations to zero and solve the resulting system: According to the paper, since the equations are nonlinear, a closed form cannot be found and optimizers need to be used to find the minimum
I have read the Matlab documentation, and I found the function fsolve to solve nonlinear equations. The problem is that there is a sum of multiple terms in those equations. Normally, I would use a for loop or a "sum" to solve those, but I have no idea how to make that within a fsolve function. Any insights on how to do it? Any advice would be greatly appreciated. Sincerely, Leo.
You may not need to solve the nonlinear equations from first principles, MATLAB has a built-in Weibull fit function. Read https://www.mathworks.com/help/stats/weibull-distribution.html https://www.mathworks.com/help/stats/wblfit.html