Statistics

Our interests cover a wide variety of areas in statistics, including both applied and theoretical problems. In addition, there are particularly strong groups in statistical genetics and Bioinformatics and environmental statistics. A special strength of the division is our commitment to interdisciplinary approaches to science. It is not uncommon for faculty to collaborate on a project with scientists from two or three other academic departments at Dalhousie University.

Statistical Genetics and Bioinformatics:


The science of Genetics is dedicated to studying the structure and function of genes and genomes, as well as the way genetic information is transmitted from generation to generation. Genetics has long been important in biology and society; however, with the rapid accumulation of gene and genome sequences, genetics has entered a new age, one which depends heavily on statistical methods. The statistical genetics group at Dalhousie University is comprised of a collection of faculty, staff, and graduate students who work together closely on methods for analyzing and interpreting genetic data.

The statistical genetics group is committed to an integrated approach to genetic and genomic science. Only through the integration of statistical, biological, mathematical, medical and computer science disciplines can the challenges of modern genetics and genomics be met. The statistical genetics group works to enhance the interdisciplinary environment at Dalhousie University through formal collaborations with other academic departments and the Center for Comparative Genomics and Evolutionary Biology. Past affiliations have included organizations such as Genome Atlantic and the CIAR Program in Evolutionary Biology. The Statistics Division provides a home for the weekly meeting of the Statistical Evolutionary Biology Group, a group of biologists, statisticians and computer scientists interested in applying statistical modeling techniques to problems in molecular evolution and comparative genomics.

Research Areas:

  • Analysis of Gene Chip Data
  • Estimation of Breeding Values
  • QTL Analysis
  • Phylogenetic Methods
  • Environmental Genomics
  • Estimating Selection Pressure on Protein Coding Sequences
  • Genomic Analysis
  • Genetics of Complex Diseases
  • Group Members and their interests:

    Joseph Bielawski Statistical Genetics| Molecular Evolution | Environmental Genomics
    C.A. Field Robust Statistics | Statistical Genetics | Data Analysis
    Hong Gu Multivariate Statistics | Machine Learning | Bioinformatics | Molecular Evolution
    David Hamilton Statistical Genetics | Linear and Nonlinear Regression | Design of
    Experiments | Data Analysis
    Christophe Herbinger Quantitative Genetics| Statistical Genetics
    Bruce Smith Time Series | Statistical Genetics | Clinical Trials
    E. Susko Molecular Evolution | Bioinformatics | Mixture Models | Machine Learning

    Environmental Statistics:


    Environmental Statistics is concerned with the development and application of statistical methods for the environmental sciences, with the purpose of addressing pressing environmental problems facing society. Its goal is the characterization and analysis of spatial and temporal variability in environmental observations, and the development of predictive models. Such models often require the fusion of discipline specific mathematical models with the techniques of statistical data analysis. An important challenge ahead lies in the development of novel techniques for the effective treatment of the wide assortment of new data streams from advanced environmental observing systems.

    By its nature, Environmental Statisitics is a multidisciplinary endeavour. The Environment Statistics group at Dalhousie is part of the new Centre for Marine Environmental Prediction (www.cmep.ca). Its goals are to develop and test new technologies for the observation, prediction and visualization of the marine environment. This provides a unique opportunity for practical applications of Environmental Statistics. Our environmental statistics work is also supported by the National Program for Complex Data Structures through a project on Spatial/Temporal Analysis of Marine Ecological Systems.

    Research Areas:

  • State space models for marine prediction
  • Tracking of marine mammal movements
  • Data assimilation and the development of operational forecast systems
  • Satellite image analysis for ocean winds and waves
  • Modelling extremes in atmosphere and oceans
  • Group Members and their interests:

    Michael Dowd Statistical Data Assimilation | Marine Environmental Data Analysis | Stochastic Ecosystem Modeling
    Chris Field Robust Statistics | Statistical Genetics | Data Analysis
    Joanna Flemming Longitudinal Data | Environmental Risk Assessment | State Space Models
    David Hamilton Statistical Genetics | Linear and Nonlinear Regression | Design of Experiments | Data Analysis
    Ron Hilburn Envirnomental Risk Assessment | Statistical Computing | Hydrologic Modeling
    Bruce Smith Time Series | Statistical Genetics | Clinical Trials
    Keith Thompson Time Series Analysis | Applications to Oceanography

    Recent Publications

    Click HERE for a list of some recent publications.



    Individual Faculty Interests

    J.P. Bielawski Statistical Genetics| Molecular Evolution | Environmental Genomics
    K.Bowen Applied Problems in Health Professions
    M. Dowd Inverse problems | Environmental Statistics | Ecosystem Modeling
    C. Field Robust Statistics | Statistical Genetics | Data Analysis
    G. Gabor Bayesian Inference
    H. Gu Multivariate Statistics | Machine Learning | Bioinformatics | Molecular Evolution
    R.P. Gupta Multivariate Analysis | Distribution Theory | Statistical Inference
    C. Herbinger Quantitative Genetics| Statistical Genetics
    D. Hamilton Linear and Nonlinear Regression | Statistical Genetics | Design of Experiments | Data Analysis
    Ron Hilburn Envirnomental Risk Assessment | Statistical Computing | Hydrologic Modeling
    B. Smith Time Series | Statistical Genetics | Clinical Trials
    E. Susko Molecular Evolution | Bioinformatics | Mixture Models | Machine Learning
    K. Thompson Time Series Analysis | Applications to Oceanography






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