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. 

Causal inference is the process of determining whether and how one variable influences another, going beyond simple correlations and attempting to uncover cause-and-effect relationships. It plays a crucial role in fields like medicine, economics, and social sciences, where understanding the impact of interventions or policies is essential.

Causal inference is the process of determining whether and how one variable influences another, going beyond simple correlations and attempting to uncover cause-and-effect relationships. It plays a crucial role in fields like medicine, economics, and social sciences, where understanding the impact of interventions or policies is essential.

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 that time, faculty have been involved in many research projects, such as the development of statistical techniques for the analysis of air pollution data to study concerns such as public health issues and global climate models, as well as collaboration with marine mammal biologists to study locations and behaviour via continuous-time tracking devices.

Forest products have a complex variability and, as a biomaterial, are inherently stochastic. Faculty in the Department analyze forest product data using advanced statistical methods in areas such as survey sampling, survival analysis, nonparametric Bayesian analysis and the handling of big data. The group has made novel contributions to statistical science that transfer to other domains and has solved long standing problems in wood science.  And something that rarely is the case - statisticians have run their own experiments and data collection. 

Machine learning and artifical intelligence have driven numerous recent, exciting, data-driven advancements in a wide range of application areas. Typical problem settings include predictive modelling (e.g., classification and regression problems) and generative modelling. Faculty in the department take a principled, rigorous approach to developing new methodologies in these areas, with applications in various scientific and engineering disciplines, and beyond.

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.