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It there any best practice to select initial values when doing maximum likelihood estimation?

For the time being I'm generating series of initial values at random using for example a normal distribution mean=0 and sd = 10, and pick the initial values with the highest maximum likelihood. A question might be how many series to generate to guarantee the global maximum. Yet this approach might be very time consuming.

I recently came across the following paper that suggest some test for global maximum oflikelihood function. But I have not yet implemented it. http://web.eecs.umich.edu/~hero/Preprints/blatt_hero_icassp05.pdf

DJJ
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  • Look through https://stats.stackexchange.com/search?q=global+optimization+answers%3A1 – kjetil b halvorsen Aug 09 '18 at 21:57
  • When it comes to clever choices of initial values for optimization routines, it seems that much can be learned from the ML community https://stats.stackexchange.com/q/229669 – Durden Jul 02 '22 at 18:24

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