Identifying profiles of physical activity behaviours in the presence of non-ignorable missing data

Physical activity and inactivity are two independent dimensions over which children aggregate into distinct behavioural profiles. Read my new article ‘Probabilistic principal component analysis to identify profiles of physical activity behaviours in the presence of non-ignorable missing data’ in the Journal of the Royal Statistical Society: Series C at


Session on Robust Methods – Call for papers, European Survey Research Association, July 2015, Reykjavik, Iceland

ESRA 2015, Reykjavik: Call for Papers – Closing date 15 January 2015

The 6th Conference of the European Survey Research Association (ESRA) will take place 13th-17th July 2015 in Reykjavik, Iceland.


Paper proposals are invited for the session on “Robust Methods in Survey Design and Analysis with Applications”

The violation of the assumptions that underlie parametric statistical methods is potentially a serious issue when drawing inferences about a population. Resulting bias in the estimates may lead to incorrect conclusions. Typical problems include, but are not limited to, the presence of outliers, untenable normality assumptions, and model misspecification.

This session aims at showcasing recent developments in robust methods for survey design and survey data analysis with emphasis on applications. Submissions on topics such as semi- and non-parametric modelling, estimation of distribution functions and quantiles, variance estimation and methods for missing data are particularly welcome. The presentations will illustrate the application of robust methods to studies in the life, social and natural sciences. Examples on the usage of related statistical software are also encouraged.

Session organizer: Marco Geraci <>

LQMM used by researchers at NASA

LQMM used by researchers at the National Aeronautics and Space Administration (NASA). The abstract cites “The data are longitudinal and result from a relatively few number of subjects; typically 10 – 20. A longitudinal study refers to an investigation where participant outcomes and possibly treatments are collected at multiple follow-up times. Standard statistical designs such as mean regression with random effects and mixed-effects regression are inadequate for such data because the population is typically not approximately normally distributed. Hence, more advanced data analysis methods are necessary. This research focuses on four such methods for longitudinal data analysis: the recently proposed linear quantile mixed models (lqmm) […]”. The full technical report is available here.