June 2007
Beginner to intermediate
950 pages
27h 8m
English
What, exactly, do we mean when we say that the parameter values should afford the ‘best fit of the model to the data’? The convention we adopt is that our techniques should lead to unbiased, variance-minimizing estimators. We define ‘best’ in terms of maximum likelihood. This notion may be unfamiliar, so it is worth investing some time to get a feel for it. This is how it works:
We judge the model on the basis how likely the data would be if the model were correct.
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