Sentence examples for missing data modelling from inspiring English sources

In other areas of multivariate statistics the role of model-based analyses is also central e.g. missing data modelling using maximum likelihood.

Although the amount of missing variables was very small, in order to include all of the collected information, the missing data modelling procedure implemented in the Mplus programme was used.

Missing data modelling in conjunction with imputational approaches could have been used to inform sensitivity analysis (i.e. assess how strong the findings are under different assumptions).

Further epidemiological and demographic data were taken from the literature and missing data modeled with DisMod II to quantify DALYs using the methods from the Global Burden of Disease (GBD) 2004 update published by the World Health Organization (WHO).

Due to their different properties, they are sensitive to many factors, such as data distribution, missing data, model size, and sample size (Hu and Bentler 1999; Fan and Sivo 2005; Barrett 2007).

Despite this, more sophisticated modelling of the missing data model could commence by allowing this interaction.

Now that model (5) has replaced model (2), we refer to model (5) as the missing data model.

Three missing data models were examined: 1. Missing completely at random (MCAR): Missingness was imposed randomly on the X variable.

This is because this model describes why data are missing, and so we also refer to this model as the missing data model.

However, appropriate analyses of data not missing at random highly depend on the choice of the postulated missing data model [ 36].

However, we will investigate the mechanism of missingness using regression models [ 20] and apply an appropriate missing data model as a sensitivity analysis [ 21].