School of Mathematics and Statistics
University of Glasgow
GLASGOW G12 8QQ
Phone: 0141 330 4046
Fax: 0141 330 4814
Nonparametric smoothing techniques, generalised additive models,
three-dimensional shape modelling, graphics, statistical computing,
computer based learning.
Selected publications and associated software.
- Bowman, A.W. & Azzalini, A. (1997).
Applied Smoothing Techniques for Data Analysis.
A suite of R functions and scripts known as the sm
package is also freely available in conjunction with the book.
- Bowman, A.W. & Wright, E. (1998).
Graphical exploration of covariate effects on survival data through
nonparametric quantile curves.
Biometrics 56, 563-570.
- Diblasi, A. & Bowman, A.W. (2001).
On the use of the variogram for checking independence in a Gaussian spatial process.
Biometrics 57, 211-218.
- McMullan, A., Bowman, A.W. & Scott, E.M. (2003).
Non-linear and nonparametric modelling of seasonal environmental data.
Computational Statistics 18, 167-183.
- Bowman, A.W. and Azzalini, A. (2003).
Computational aspects of nonparametric smoothing with illustrations from the sm library.
Computational Statistics and Data Analysis 42, 545-56.
- Bock, M. & Bowman, A.W. (2006). On the measurement
and analysis of asymmetry with applications to facial modelling.
Applied Statistics 55, 77-91.
- Giannitrapani, M., Bowman, A.W., Scott, E.M. & Smith, R. (2005).
Sulphur dioxide in Europe: the relationship between emissions and measured concentrations.
Atmospheric Environment, 40, 2524-2532.
- Bowman, A.W. & Bock, M. (2006). Exploring variation in three-dimensional shape data.
J.Comp.Graph.Stat., 15, 524-541.
- Bowman, A.W., Crawford, E. Bowman, R.W. (2006).
rpanel: making graphs move with tcltk.
R News; 6, issue 4.
- Bowman, A.W. (2007). Comparing nonparametric surfaces.
Statistical Modelling; 6, 1-21.
- Giannitrapani, M., Bowman, A.W., Scott, E.M. & Smith, R. (2007).
Temporal analysis of spatial covariance of SO2 in Europe from 1990 to 2001.
Environmetrics; to appear.
- Bowman, A.W., Pope, A. & Ismail, B. (2007).
Detecting discontinuities in nonparametric regression curves and surfaces.
Statistics and Computing; 16, 377-390.
- Bock, M., Bowman, A.W. & Ismail, B. (2007).
Estimation and inference for error variance in bivariate nonparametric regression.
Statistics and Computing; to appear.
- Ferguson, C.A., Bowman, A.W., Scott, E.M. & Carvalho. L. (2007).
Model comparson for a complex ecological system.
J.Roy.Statist.Soc., Series A; to appear.
- Bowman, A.W., Crawford, E., Alexander, G. & Bowman, R.W. (2006).
rpanel: simple interactive controls for R functions using the tcltk package.
Journal of Statistical Software 17, issue 9.
Current research reports.
- Giannitrapani, M., Bowman, A.W., & Scott, E.M. (2005).
Additive models for correlated data with applications to air pollution monitoring.
- McMullan, A., Bowman, A.W. & Scott, E.M. (2006).
Water quality in the River Clyde: a case study of additive and interaction models.
- Barry, S.J.E. & Bowman, A.W. (2006).
Linear mixed models for longitudinal shape data with applications to facial modelling.
- Ferguson, C.A. & Bowman, A.W. & Scott, E.M. & Carvalho, L. (2006).
Multivariate varying-coefficient models for ecological systems.
- Ferguson, C.A., Carvalho, L., Scott, E.M., Bowman, A.W. & Kirika, A. (2006).
Assessing ecological responses to environmental change using statistical models.
- Bowman, A.W., Giannitrapani, M. & Scott, E.M. (2007).
Spatiotemporal smoothing and and sulphur dioxide trends over Europe.
- O'Donnell, D., Bowman, A.W., Scott, E.M. & Hallard, M. (2009).
Stream Distance Based Prediction on River Networks.
Teaching related projects
The rpanel package
provides a set of simple
interactive controls for R functions which are particularly useful in creating
dynamic graphics. The package is intended for general use but there is a particularly
strong application in a teaching context. The web page for the project gives a
variety of examples.
The Department of Statistics houses the Glasgow branch of the
Higher Education Academy MSOR network.
The Glasgow activity provides information on, and resources for, the use of
technology in the teaching and learning of statistics.
The STEPS project was created by a
consortium of eight UK university departments who developed problem-based learning
materials in Statistics, delivered on the computer.