The Bayesian paradigm for statistical inference uses expert knowledge, formulated in terms of probability distributions of unknown parameters of interest.   These distributions, called prior distributions, are combined with data to provide new information about parameters, via new parameter distributions called posterior distributions.  One research theme centers on devising new Bayesian methodologies, i.e., new statistical models with which Bayesian inferences can provide particular scientific insight.

Recent advances of -omics technologies have stimulated a large body of biomedical studies focused on the discovery and characterization of molecular mechanisms of various diseases. For example, many studies have been focused on the identification of genes to diagnose or predict cancer. The rapid expansion of complex and large -omics datasets has nourished the development of tailored statistical methods to address the challenges that have arisen in the field.

Many faculty members work on applying statistical methods to biomedical problems, ranging from analysing gene expression data to public health issues. Much of this work is done in conjunction with local hospitals (such as St Paul's) and research institutes (such as the BC Cancer Agency and the BC Genome Sciences Center).  In the fall of 2009, we introduced the biostatistics option to our  MSc program, an option that is joint with the School of Population and Public Health. 

Data Science information page.

The Department has a long history of research and collaborations in Environmental Statistics and in Spatial Statistics, beginning with Jim Zidek's pioneering work with the United States Environmental Protection Agency.

Since 2009, more than 60 researchers have been a part of this group, studying the properties of wood products, working on projects such as the development of engineering standards, monitoring for changes in product properties over time, subset selection methods for species grouping in the marketing of lumber and the duration of load effect in construction. The group is made up of  statisticians from UBC and SFU - faculty, students and staff - and collaborating scientists at FPInnovations Vancouver, funded by Collaborative Research and Development Grants awards under NSERC’s Forest

Modern multivariate and time series analyses go beyond the classical normality assumption by modelling data that could combine binary, categorical, extreme and heavy-tailed distributions. Dependence is modeled non-linearly,  often in terms of copula functions or stochastic representations. Models for multivariate extremes arise from asymptotic limits.  Characterization and modelling of dependence among extremes as well as estimation of probabilities of rare events are topics of on-going research.

Statistical procedures are called robust if they remain informative and efficient in the presence of outliers and other departures from typical model assumptions on the data.    Ignoring unusual observations can play havoc with standard statistical methods and can also result in losing the valuable information gotten from unusual data points.  Robust procedures prevent this.  And these procedures are more important than ever since currently, data are often collected without following established experimental protocols.  As a result, data may not represent a single w

Statistical learning, sometimes called machine learning, is becoming ever more important as a component of data science, and department members have had active research in this area for more than a decade.  Statistical learning methods include classification and regression (supervised learning) and clustering (unsupervised learning).  Current research topics of faculty members and their graduate students include construction of phylogenetic trees in evolution, ensembles of models and sparse clustering.  Applications include the search for novel pharmaceutical drugs and detect