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Courses

Bayesian Modelling and Computation Part One

Bayesian Modelling and Computation Part One

The two courses are primarily aimed at statisticians who wish to use Bayesian methods in their data analysis and modelling problems. The courses will be suitable for statisticians from government departments, practitioners from industry, and research students at all levels. Academic researchers and scientists from other disciplines can also attend but should have a very strong background in statistics/mathematics to fully understand the whole course.

Please see more info page for citeria for booking on 2 part course.

Course 1: Bayesian Modelling and Computation, June12-14 2017 The first short-course on "Bayesian Modelling and Computation" is aimed at applied scientists who are thinking of using Bayesian methods and would like to receive a gentle introduction with a large practical component.

No previous knowledge of Bayesian methods is necessary. However, some 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.

Course 2: Hierarchical modelling of spatial and temporal data.

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.

The course concludes with a brief discussion of spatio-temporal and spatial survival models, both illustrated in the context of cancer control and epidemiology. Computer implementations for the models via the WinBUGS and R packages will be described throughout.

Participants are encouraged to buy the book Hierarchical Modeling and Analysis for Spatial Data, co-authored by Professor Gelfand.

StartEndPlaces LeftCourse Fee 
12/06/201714/06/2017[Read More]
Computing and Modelling with R

Computing and Modelling with R

Day 1: An Introduction to Statistical Computing with R 

Day 2: Applied Statistical Modelling with R 

Day 3: Data Visualisation with R.

The course is split into three days; participants can attend one day or more. All days will consist of interactive workshops, together with  time for guided computational practice on the material, supported by the lecturer and additional experts on the R language. Lunch will be provided on each day. Computers are provided, or participants can use their own laptop.

 Day 1 is suitable for people with no experience of R, and will be an introduction to programming in R. There is little mathematical statistical knowledge assumed, and will be an introduction to the programming language.

 Day 2 will be suitable for those that have attended Day 1, or who have some previous experience in  R. It will give an overview of statistical modelling in R.

 Day 3 will focus on more advanced techniques for programming in R. It will focus on methods for visualisation in data science, with applications driven from Biological applications, and assumes some programming knowledge in R, such as that from Day 1 of the course.

StartEndPlaces LeftCourse Fee 
24/05/201726/05/2017[Read More]
Hierarchical Modelling of Spatial and Temporal Data Course 2

Hierarchical Modelling of Spatial and Temporal Data Course 2

Course 2: Hierarchical modelling of spatial and temporal data.

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.

The course concludes with a brief discussion of spatio-temporal and spatial survival models, both illustrated in the context of cancer control and epidemiology. Computer implementations for the models via the WinBUGS and R packages will be described throughout.

Participants are encouraged to buy the book Hierarchical Modeling and Analysis for Spatial Data, co-authored by Professor Gelfand.

StartEndPlaces LeftCourse Fee 
15/06/201716/06/2017[Read More]
Linear Mixed and Generalized Linear Mixed Models with Applications in Medicine 2017

Linear Mixed and Generalized Linear Mixed Models with Applications in Medicine 2017

This course will provide an overview of the current ideas in linear mixed models (and generalized linear models) and their manifold medical/health applications with a continuous outcome appropriate for analysing studies with simple and more complex hierarchical data structure such as such as nested fixed or random effects.

StartEndPlaces LeftCourse Fee 
12/12/201713/12/2017[Read More]
Statistical Methods for Meta-Analysis 2017

Statistical Methods for Meta-Analysis 2017

The course will address the question of single-zero or double zero studies which can occur in one or both arms of a trial involved in the meta-analysis.

The course will focus on Mantel-Haenszel techniques as well on Poisson regression models which all allow the occurrence of zero-events in one or both arms. Zero-inflation models are also covered .

This course will use the package STATA throughout.

StartEndPlaces LeftCourse Fee 
19/09/201720/09/2017[Read More]
Statistical Methods in Diagnostic Studies course 2017

Statistical Methods in Diagnostic Studies course 2017

The aim of the course is for attendees to be able to understand and use a broad range of statistical methods that may be used in the testing of a new diagnostic device or procedure.

StartEndPlaces LeftCourse Fee 
30/05/201730/05/2017[Read More]
Techniques and Strategies for Survey Implementation in Developing Countries

Techniques and Strategies for Survey Implementation in Developing Countries

Developing countries are an especially challenging environment for administering surveys, though survey data is usually the most important kind of information about the socio-economic conditions in these countries. In this short course, we will address these challenges, and provide solution strategies that have been successfully applied in various countries with a particular focus on the application of contemporary developments in survey methodology.

StartEndPlaces LeftCourse Fee 
19/06/201724/06/2017[Read More]
S3RI logo

The Craft of Smoothing

In the course, we describe in detail the basics and use of P-splines, as a combination of regression on a B-spline basis and difference penalties (on the B-spline coefficients). Our approach is practical. We see smoothing as an everyday tool for data analysis and statistics. We emphasize the use of modern software and we provide functions for R.

 

Session 1 presents the idea of bases for regression. It will show why global bases, like power functions or orthogonal polynomials are ineffective and why local bases (Gaussian bell-shaped curves or B-splines) are attractive. In Session 2, penalties are introduced, as a tool to give complete and easy control over smoothness. The combination of B-splines and difference penalties will be studied for smoothing, interpolation and extrapolation. In these first two sessions the data are assumed to be normally distributed around a smooth curve. In Session 3, we extend P-splines to non-normal data, like counts or a binomial response. The penalized regression framework makes it straightforward to transplant most ideas from generalized linear models to P-spline smoothing. Important applications are density estimation and variance smoothing. Any smoothing method has to balance fidelity to the data and smoothness of the fitted curve. An optimal balance can be found by cross-validation or AIC. This subject is studied in Session 4, as well as the computation of error bands of an estimated curve. We also show how optimal smoothing performs on simulated data, to give you confidence in that it makes the right choices. In the first four sessions we only consider one-dimensional smoothing. When there are multiple explanatory variables, we can use generalized additive models, varying-coefficient models, or combinations of them. Tensor products of B-splines and multi-dimensional difference penalties make an excellent tool for smoothing in two (or more) dimensions. This is the subject of session 5. The final Session 6 looks at the use of P-splines in regression problems with very many variables, which are ordered, like in optical spectra. In the chemometric literature this is known as multivariate calibration. In addition there will be two computer lab sessions, in which R software will be used to solve a number of smoothing problems. One session will concentrate on simple functions with limited goals. This will improve your understanding of what is going on “under the hood". This session will continue and apply smoothing to the generalized linear model and density estimation. The second lab will be provided that uses the mgcv package, written by Simon Wood, a large but powerful tool that can handle a variety of situations, including generalized additive modeling. The second lab will continue with full 2D P-spline smoothing for normal and binomial responses.

StartEndPlaces LeftCourse Fee 
10/07/201711/07/2017[Read More]

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