Associate Professor in Statistics
Yves Berger's research focuses on
foundations of statistical inference from complex sample
surveys. Yves Berger established a world-class research
related to various fundamental issues of statistical
inference from complex sample surveys. These issues
include: variance estimation; repeated surveys;
non-response; imputation; inference for complex
parameters (empirical likelihood). His publications
include articles in the Journals of the Royal
Statistical Society (Series A, B and C), Biometrika and
the Canadian Journal of Statistics.
Yves Berger teaches a wide range of courses at
undergraduate and postgraduate levels: statistical
modelling, generalised linear models, sample survey
theory, non-response adjustment, multivariate analysis,
multilevel models, longitudinal data and repeated
measures, statistical computing, computer intensive
statistical methods, statistical consulting,
communication and research skills and demography.
Latest paper: "modelling complex survey data with population level information: an empirical likelihood approach" (Biometrika 2016)
Posted on 24th July 2017
Survey data are
often collected with unequal probabilities from a
stratified population. In many modelling situations, the
parameter of interest is a subset of a set of
parameters, with the others treated as nuisance
parameters. We show that in this situation the empirical
likelihood ratio statistic follows a chi-squared
distribution asymptotically, under stratified single and
multi-stage unequal probability sampling, with
negligible sampling fractions. Simulation studies show
that the empirical likelihood confidence interval may
achieve better coverages and has more balanced tail
error rates than standard approaches involving variance
estimation, linearization or re-sampling.
Follow this link to download the paper
