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Given a random variable $X$ which arise from a parameterized distribution $F(X;θ)$, the likelihood is defined as proportional to the probability of observed data as a function of $θ$: $\operatorname{L}(θ | x)=\operatorname{P}(X=x \mid θ)$

4 votes
1 answer
12k views

AIC, likelihood, loglikelihood confusion

So are the likelihood values. Are the log-likelihood values positive or negative? … [1] 90785.92 > fit1$AIC [1] 90909.47 > fit2$AIC [1] 90839.92 # AIC from likelihood, par1 refers to number of fitted parameters > 2*par1-2*log(fit1$likelihood) [1] -14.8344 > 2*par2-2*log(fit2$likelihood
praseodyymi's user avatar
1 vote
1 answer
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AIC and likelihood statistics from tbats (forecast-package in R)

> fit1$likelihood [1] 90854.67 > fit1$AIC [1] 90884.67 > par1 # par1 <- length(fit1$parameters$vect), number of fitted parameters [1] 4 > 2*par1-2*log(fit1$likelihood) # AIC [1] -14.83403 > fit2$likelihood … But, the likelihood for fit1 is bigger than for fit2. What should I believe? …
praseodyymi's user avatar