Of course, in MLE, if we collect more and more observations data, MLE will perform better and better. But is there a way to find a bound on parameter estimate errors in order to decide when to stop collect data/observations, say 1000 observations are great, but 500 observations is good enough... Thanks!
Ben Bolker
2009-May-29 12:27 UTC
[R] how do I decide the best number of observations in MLE?
losemind wrote:> > Of course, in MLE, if we collect more and more observations data, MLE > will perform better and better. > > But is there a way to find a bound on parameter estimate errors in > order to decide when to stop collect data/observations, say 1000 > observations are great, but 500 observations is good enough... > >This general area is called _power analysis_. There is no sharp cutoff, but people often pick some threshold (such as reducing the probability of type II errors below 20%, i.e. power=80%, or specifying some cutoff on the standard error of a parameter ... for simple cases, there are analytic solutions (apropos("power")), for more complex cases this generally has to be done by simulation. -- View this message in context: http://www.nabble.com/how-do-I-decide-the-best-number-of-observations-in-MLE--tp23773503p23779047.html Sent from the R help mailing list archive at Nabble.com.