Category Archives: robust statistics

My new paper on the decomposition of the indirect effect in mediation analysis using quantiles

A novel quantile-based decomposition of the indirect effect in mediation analysis with an application to infant mortality in the US population by M Geraci and A Mattei

In mediation analysis, the effect of an exposure (or treatment) on an outcome variable is decomposed into two components: a direct effect, which pertains to an immediate influence of the exposure on the outcome, and an indirect effect, which the exposure exerts on the outcome through a third variable called mediator. Our motivating example concerns the relationship between maternal smoking (the exposure, X), birthweight (the mediator, M), and infant mortality (the outcome, Y), which has attracted the interest of epidemiologists and statisticians for many years. We introduce new causal estimands, named u-specific direct and indirect effects, which describe the direct and indirect effects of the exposure on the outcome at a specific quantile u of the mediator, 0 < u < 1. Under sequential ignorability we derive an interesting and novel decomposition of u-specific indirect effects. The components of this decomposition have a straightforward interpretation and can provide new insights into the complexity of the mechanisms underlying the indirect effect. We illustrate the proposed methods using data on infant mortality in the US population. We provide analytical evidence that supports the hypothesis that the risk of sudden infant death syndrome is not predicted by changes in the birthweight distribution.


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 <>


Median percent change

A typical summary statistic for temporal trends is the average percent change (APC). The APC is estimated by using a generalized linear model, usually under the assumption of linearity on the logarithmic scale. A serious limitation of least-squares type estimators is their sensitivity to outliers. We propose a robust and easy-to-compute measure of the temporal trend based on the median of the rates (median percent change – MPC), rather than their mean, under the hypothesis of constant relative change.

Day on Quantile Regression at the Royal Statistical Society – London, 29 May

An exciting one-day meeting organized by the RSS General Applications Section will take place in London on 29th May 2013. In the morning, the workshop will have a tutorial in quantile regression with hands-on using the R package quantreg. The research session in the afternoon will have three excellent speakers. Full programme and registration details here.