Dear All,
Please find below some upcoming courses at the University of Southampton for 2019:
Multivariate Analysis 10th – 11th July 2019
This course provides a practical introduction to the analysis of high dimensional data, consisting of correlated variables. Objectives may include data reduction, summarisation, searching for groupings and classification, which are common in the areas of unsupervised and supervised learning. The course sequentially covers a set of classic multivariate analysis techniques for meeting these objectives, starting with principal components analysis and finishing with partial least squares. R, SAS, SPSS or Stata may be used for practical work.
More information: https://www.southampton.ac.uk/s3ri/cpd/courses/10-07-19-multivariate-analysis.page
To Book: http://go.soton.ac.uk/acc
Survival Analysis for Medical and Health Professionals 25th – 26th September 2019
This course provides a practical introduction to the analysis of data in the form of time-to-event, or survival times. Such data is frequently highly skewed and times may be censored. These features, together with clinical questions in a survival context, require dedicated statistical techniques. This course begins with an overview and continues to cover the following topics: summary statistics and exploratory graphics, simple hypothesis testing, regression modelling using the Cox model and some extensions to this model. R, SAS, SPSS or Stata may be used for practical work.
More Information: https://www.southampton.ac.uk/s3ri/cpd/courses/25-09-19-survival-analysis-for-medical-and-health-professionals.page
To Book: http://go.soton.ac.uk/aa9
Introduction to Bayesian Hierarchical Modelling and Computation & Hierarchical modelling of spatial and temporal data 16th – 20th September 2019
Course 1:
Introduction to Bayesian Hierarchical Modelling and Computation, September 16-17, 2019.
Lecturer: Sujit Sahu The first short-course is aimed at applied scientists (with good graduate degrees) who are thinking of using Bayesian methods and would like to receive a gentle introduction with a large practical component using R, WinBuGS and STAN.
No previous knowledge of Bayesian methods is necessary. However, familiarity with standard probability distributions (normal, binomial, Poisson, gamma) and standard statistical methods such as multiple regression will be assumed.
Theory lectures on the Bayes theorem, elements of Bayesian inference, choice of prior distributions and introduction to MCMC will be followed by hands-on experience using R and the WinBUGS software. Some of the data analysis examples discussed here will be enhanced by using spatial statistics methods in the second course.
More advanced methods using reversible jump and INLA will also be introduced.
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Course 2: Hierarchical modelling of spatial and temporal data. September 18-20, 2019
Lecturers: Prof Alan Gelfand (Duke University, USA) and Sujit Sahu This course will provide an overview of current ideas in statistical inference methods appropriate for analysing various types of spatially point referenced data, some of which may also vary temporally.
The course begins with an outline of the three types of spatial data:
point-level (geostatistical), areal (lattice) and spatial point process, illustrated with examples from environmental pollution monitoring and epidemiological disease mapping.
Exploratory data analysis tools and traditional geostatistical modelling approaches (variogram fitting, kriging, and so forth) are described for point referenced data, along with similar presentations for areal data models. These start with choropleth maps and other displays and progress towards more formal statistical concepts, such as the conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models so often used in conjunction with spatial disease mapping.
The heart of the course will cover hierarchical modelling for spatial response data, including Bayesian kriging and lattice modelling. More advanced issues will also be covered, such as nonstationarity (mean level depending on location) and anisotropy (spatial correlation depending on direction as well as distance). Bayesian methods will also be discussed for modelling data that are spatially misaligned (say, with one variable measured by post-code and another by census tract), since they are particularly well-suited to sorting out complex interrelationships and constraints.
This course will have a large practical component on model based data analysis using spBayes, spTimer, CARBayes and STAN.
For further information including details for fees and registration, please visit the web page http://www.southampton.ac.uk/~sks/2019course/
If you have any further questions with regards to these courses please email [log in to unmask]
Kind Regards
Andrew Cox
Administrative officer
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