By David R. Anderson
I take advantage of this ebook as a supplementary textual content for a graduate path. it is in actual fact written, and explains all the a number of version AIC-based strategy in basic terms that scholars that are not specially quantitatively orientated can comprehend and follow the concepts.
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Additional resources for Model Based Inference in the Life Sciences: A Primer on Evidence
There is a large literature admonishing investigators to avoid overfitting as this leads to spurious effects and imprecision. An equally large literature warns of underfitting because of bias and effects that are present, but missed during data analysis. Until somewhat recently, statistical science lacked an effective way to objectively judge the trade-off – how many are too many, how many are not enough. This has been largely resolved for a wide class of problems and is another example of the advantage (actually necessity) of quantification.
Investigators should make every attempt to garner the resources to allow an adequate sample size to be realized. There is a large literature on the establishment of sample size, given either some background data from a small pilot survey or outright considered guesses about the system to be studied (see Eng 2004). Monte Carlo simulation studies provide another means to predict the sample size for a particular application (see Muthen and Muthen 2002). 2). While the sample size here is moderate (n = 319), ˆ ) varies by a factor of 88; thus estimates of the per year force of infection (λ it seems realistic that the data might be adequate to reveal some interesting insights.
I will say little about this approach. The much more general approach is Fisher’s maximum likelihood (see Appendix A). ” Hence the name maximum likelihood estimate (MLE); it is the value of the parameter that is most likely, given the data and model. As sample size increases (asymptotically), MLEs enjoy several properties (within certain regularity conditions): unbiased, minimum variance, and normally distributed. In addition, if one takes an MLE and transforms it to another estimate, it too is an MLE (the “invariance” property).