[Date Index] [Thread Index] [Author Index]

Maximum likelihood and experimental data

• To: mathgroup at smc.vnet.net
• Subject: [mg114804] Maximum likelihood and experimental data
• From: max spoor <max.spoor73 at gmail.com>
• Date: Fri, 17 Dec 2010 03:31:22 -0500 (EST)

Dear all,

I am new  to Mathematica and have a basic question about comparing the
fit of different models to
experimental data on actual frequencies of play in 2x2 games (e.g. I
compare predictions like [0.439; 0.604] to frequencies like [.455; .
636]).

Some of the models are non parametric, while others are, so I would
like to "punish" the latter for abundance of parameters, say using the
Akaike information criterion for likelihood estimation.

My question is what distribution should I assume about the underlying
data? Also, is there a requirement in terms of the minimum number of
observations?

Any suggestions or code example will help!

max