Sentence examples for maximum likelihood modelling from inspiring English sources

Censored normal distributions showed a good fit to the data using maximum likelihood modelling.

Multiple imputation based on weight and response, full maximum likelihood modelling using information on weight and full maximum likelihood modelling where the proportion of males among the individuals lacking information about sex was estimated (EST) gave precise and unbiased estimates in the presence of missing data when data were missing completely at random or missing at random.

Methods using multiple imputations or full maximum likelihood modelling yield similar results when handling missing data in linear fixed effect models, when the methods are implemented in comparable ways [ 4, 5, 22].

Multiple imputation methods and methods based on maximum likelihood modelling give less bias and higher precision in parameter estimates than simpler methods when handling missing data in linear fixed effect models [ 4, 5].

Multiple imputations and full maximum likelihood modelling of continuous missing covariates can be done in a similar way as multiple imputations and full maximum likelihood modelling of categorical missing covariates.

Statistical analysis used SPSS 11.5, with maximum likelihood modelling of censored distributions carried out using a purpose-written Excel spreadsheet (see Additional File 1).

BM: Binormal maximum likelihood modeling.

AUROC Area under the receiver operator characteristic curve BM Binormal maximum likelihood modeling LR Logistic regression SD Standard Deviation UBT Urea Breath Test None declared.

It is demonstrated that the maximum likelihood model is highly accurate in reservoir performance prediction.

We present a maximum likelihood model to estimate the age of retrotransposon subfamilies.

The deviance statistic is defined as: -2*log(Maximum Likelihood (model)) (9).