Read my new article ‘Aranda-Ordaz quantile regression for student performance assessment’ in the Journal of Applied Statistics at http://www.tandfonline.com/doi/full/10.1080/02664763.2015.1025724.
Read my new article ‘Improved transformation-based quantile regression’ in the Canadian Journal of Statistics at http://onlinelibrary.wiley.com/doi/10.1002/cjs.11240/abstract!
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 <email@example.com>
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.
Read my #OpenAccess article ‘A Gradient Search Maximization Algorithm for the Asymmetric Laplace Likelihood’ at http://bit.ly/1j99BKN!
The new version of lqmm with bug fixes, amendments and new features is now on CRAN. A vignette is also available Geraci M (2014). Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression, Journal of Statistical Software 57(13), 1-29.