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The ROC analysis was applied to calibrate score cut-offs by estimating a likelihood ratio positive (LR+) for each distinct score cut-off.
Risk assessment was conducted on all steps required to administer follitropin alfa using the redesigned pen and by rating the risk level according to FMEA scores [10]; this was derived by estimating a likelihood of occurrence on a scale of 1 – 10 (10 = very likely to occur) and a severity rating of 1 – 10 (10 = most severe).
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We allow for endogeneity of intensity and length of stay by estimating a quasi-maximum-likelihood discrete factor model, where the distribution of the unmeasured variable is modeled using a discrete distribution.
The model was constructed by estimating a Cox regression model, maximising a likelihood function with a ridge (sum of squared regression coefficients) penalty term to prevent against over-fitting potentially caused by the small number of events in our study (76 events).
Assuming normality of the error term u we can (up to scale) identify the parameters of the model by estimating a probit model by maximum likelihood.
We start by estimating a probit model to predict the likelihood of being a returnee based on age, gender and province of birth, and then we match individuals based on treatment status.
Therefore we tested branches leading to species with larger testes, as a proxy of sperm competition, by estimating the likelihood of a model with estimated ω along the foreground and background branches against a model with the foreground branch ω fixed at 1.0.
For individual gene pairs, the deviation of the dN / dS ratio from 1.0 was measured in HYPHY (Pond et al. 2005), by estimating the likelihood of a constrained (dN / dS = 1.0) and a free model.
For each edge in the network, we calculated a confidence score by estimating the likelihood of this edge getting recovered under removal (bootstrap value).
Therefore, the transcript quantification from RNA-Seq data was performed using the Expectation-Maximization method (RSEM), which handles read mapping uncertainty with a statistical model by estimating maximum likelihood expression levels [ 58, 65].
Brochier et al. (2002) developed a method to assess incongruence by estimating the likelihoods for a pool of tree topologies with a large number of genes.
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