Each trial included a minimum of 9 images (i.e., 18 s) to reduce the likelihood of nonlinear summing of overlapping hemodynamic responses.
The advantage of such a method is that it allows for getting the likelihood function sum of the GARCH univariate models.
For maximum likelihood, the sum of the log likelihoods are evaluated for one-, five- and ten-day returns using overlapping data for the five and ten day returns.
Following the profile likelihood or least squares approach, the optimal value of τ may be found by proceeding through the candidate values, estimating the other parameters and the likelihood or sum of squared errors at each value.
Maximization of this likelihood minimizes the sum of the moduli of the residuals (rather than their squares) on the basis that the testable information is restricted to the expected value of the modulus of the difference between theory and experiment.
At a given break-even discount rate, the length of time over which benefits are paid is unrelated to the likelihood of choosing the lump sum.
a k (selection parameter) values from MLHKA test; k<1 means a reduction in polymorphism while k>1 means an excess of nucleotide variation; b p values for the MLHKA test; c likelihood (sum of deviations) for multi-locus HKA test; d p values from demographic coalescent simulations for multi-locus HKA; e not analyzed.
Similarly, Matras sums the likelihood of matching with a difference d (same value considered by Dali) taking into account the site distances over the chains considering all site pairs of a protein.
k = number of parameter estimates, RSS = residual sum of squares, AICc = Akaike information criterion adjusted for the sample size, w = conditional model probability (likelihood of model i divided by the sum of model likelihoods).
UV = UV index, Temp = average maximum temperature, RH = relative humidity, AtmP = atmospheric pressure, PC1 and PC2 = genetic principal components, k = number of parameter estimates, RSS = residual sum of squares, AICc = Akaike information criterion adjusted for the sample size, w = conditional model probability (likelihood of model i divided by the sum of model likelihoods).
