ONLINE COURSE – Missing Data Analytics (MDAR01) This course will be delivered live
https://www.prstatistics.com/course/online-course-missing-data-analytics-mdar01-this-course-will-be-delivered-live/ Email oliverhoo...@prstatistics.com 8 September 2021 - 10 September 2021 TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhoo...@prstatistics.com for full details or to discuss how we can accommodate you). Course overview: This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular mice package. The course is structured over 3 days and includes classes on: An introduction to missing data analysis terminology, missing completely at random, missing at random, not missing at random A revision of likelihood and regression approaches The Fully Conditional Specification (FCS) approach An introduction to the mice package The use of Bayesian and likelihood-based methods in missing data analysis Bayesian missing data analysis using JAGS More advanced missing data analysis including non-ignorable and not missing at random methods Wednesday 8th – Classes from 09:30 to 17:30 How to run a missing data analysis in mice • Introduction to Bayesian analysis and missing data • The use of Bayesian and likelihood-based methods in missing data analysis • The fully conditional specification approach to missing data analysis Thursday 9th – Classes from 09:30 to 17:30 Including missing data in JAGS and Stan • An introduction to the mice package • Bayesian software tools JAGS/Stan for missing data analysis Friday 10th – Classes from 09:30 to 17:30 Advanced missing data analysis methods • More advanced missing data analysis including non-ignorable and not missing at random methods • Missing data analysis in machine learning -- Oliver Hooker PhD. PR statistics 2020 publications; Parallelism in eco-morphology and gene expression despite variable evolutionary and genomic backgrounds in a Holarctic fish. PLOS GENETICS (2020). IN PRESS www.PRstatistics.com facebook.com/PRstatistics/ twitter.com/PRstatistics 53 Morrison Street Glasgow G5 8LB +44 (0) 7966500340 +44 (0) 7966500340 _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
