Experience

This page lists my statistical experience (main points).

Methodology:

diagnostics,

mixed model estimation,

approximate inference in nonlinear mixed models

empirical process theory,

classical goodness-of-fit tests,

sampling techniques, frequentist and Bayesian,

contingent valuation,

nonlinear regression, dose-response curves (applications in weed science).

linear regression models,

mixed models, both linear and nonlinear,

repeated measurements models,

generalised linear models.

SAS: proc genmod, proc glm, proc lifereg, proc loess, proc mixed, proc nlin, proc nlmixed, glimmix, nlinmix

R: anova, glm, lm, lme, nlme.

class teacher in experimental design (Spring semester for 5 consecutive years),

class teacher in basic statistics, both at University of Copenhagen and at Copenhagen Business School

statistical consulting for bachelor, master thesis and PhD students,

guest lecture on statistical analysis of contingent valuation data,

internal department seminars on inference in mixed models, diagnostics in mixed models and Polya posteriors,

talk at EMS in Prague,

seminar at School of Statistics, University of Minnesota.

Last updated: 22/1-'05