Doctor of Philosophy in Statistics (PhD)
Established in 1983, the Department of Statistics at UBC is internationally renowned for its excellence in research and the high calibre of its faculty members. Our programs offers students different options for pursuing their interests and professional goals. Students completing our PhD program will be well-prepared for a job in industry, government or academia. During their program our students develop important professional skills that include: effective communication skills for both technical and non-technical audiences, creativity and originality, and grant writing skills, among others. They also acquire a broad knowledge of modern statistical methods, including computing and data management.
What makes the program unique?
The Department is renowned in Canada for its research excellence and its leadership in the research community. Students are engaged through both courses and research, and develop a strong set of skills, both applied and theoretical. The Department has always valued data driven research, consulting and collaboration, and has long held communication and computing skills as crucial for success. Graduate students participate actively in our research, teaching and consulting activities, and enjoy a wide variety of opportunities for interaction with other researchers and students on- and off-campus. In addition, our graduate students run their own statistical consulting service, which provides them with professional (paid) experience even before they finish their program.
We have recently introduced a highly innovative qualifying process – instead of writing an exam, first year PhD students register in a reading and research course where they work on research papers proposed by individual faculty members.
Contact the program
Admission Information & Requirements
1) Check Eligibility
Minimum Academic Requirements
The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:
Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.
English Language Test
Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.
Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:
TOEFL: Test of English as a Foreign Language - internet-based
Overall score requirement: 100
IELTS: International English Language Testing System
Overall score requirement: 7.5
Other Test Scores
Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:
The GRE is not required.
Prior degree, course and other requirements
Successful PhD applicants typically have an MSc in Statistics or an MSc or PhD in Mathematics with strong evidence of interest in statistics. A student with only a Bachelors degree cannot usually be admitted to our PhD program, but rather must first enter the MSc program, either first completing the MSc or applying for transfer to the PhD after one year. If you have only had a few courses in statistics, your application to the PhD program will not be successful. For admission to the PhD program, the Admissions committee requires the following, in addition to the requirements for admission to the MSc program. a course in advanced statistical inference courses in rigorous mathematics at least 3 of the following courses at the graduate level: stochastic processes, advanced probability, mathematical statistics, linear models The above requirements are in addition to the minimum admission requirements of the Faculty of Graduate and Postdoctoral Studies. Please note that meeting our admission requirements does not guarantee admission. The following background will strengthen the application. courses in real analysis, and possibly measure theory, advanced probability (limit theorems, sigma fields); a broad range of courses in statistical methods (e.g., some topics among statistical computing, Bayesian statistics, generalized linear models, time series, multivariate statistics); undergraduate or graduate computer science courses; research or work experience relevant to statistics; solid programming experience (e.g., C, C++, Fortran, Python, R, SAS, Matlab).
We require a 2 page (maximum) statement of interest/research proposal, as well as a CV.
2) Meet Deadlines
3) Prepare Application
All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.
Letters of Reference
A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.
Statement of Interest
Many programs require a statement of interest, sometimes called a "statement of intent", "description of research interests" or something similar.
Students in research-based programs usually require a faculty member to function as their supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.
Instructions regarding supervisor contact for Doctor of Philosophy in Statistics (PhD)
Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.
4) Apply Online
All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.
Faculty are conducting research in a variety of applied an theoretical areas, such as Bayesian Statistics, Bioinformatics, Biostatistics, Environmental and Spatial Statistics, Forest Products Stochastic Modeling, Modern multivariate and time series analysis, robust statistics, and Statistical learning. Further details can be found on our website: https://www.stat.ubc.ca/research-areas
During the first year of the program, students will complete Stat 548, the Qualifying Course. This directed reading course consists of reading and reporting on five papers under the supervision of different faculty members. This unique course allows students the opportunity to explore a diverse range of Statistical topics and work with different faculty members before committing to a supervisor and thesis research topic. The PhD Comprehensive Exam will take place by the end of the second year in the program. This exam lays the groundwork for the PhD thesis, which consists of independent original research. Students are expected to have completed all required courses before the Comprehensive Exam. Near the end of thesis completion, students present their work at the Department Seminar.
Tuition & Financial Support
|Fees||Canadian Citizen / Permanent Resident / Refugee / Diplomat||International|
|Installments per year||3||3|
|Tuition per installment||$1,732.53||$3,043.77|
|Tuition per year|
(plus annual increase, usually 2%-5%)
|Int. Tuition Award (ITA) per year (if eligible)||$3,200.00 (-)|
|Other Fees and Costs|
|Student Fees (yearly)||$969.17 (approx.)|
|Costs of living (yearly)||starting at $17,242.00 (check cost calculator)|
All fees for the year are subject to adjustment and UBC reserves the right to change any fees without notice at any time, including tuition and student fees. Tuition fees are reviewed annually by the UBC Board of Governors. In recent years, tuition increases have been 2% for continuing domestic students and between 2% and 5% for continuing international students. New students may see higher increases in tuition. Admitted students who defer their admission are subject to the potentially higher tuition fees for incoming students effective at the later program start date. In case of a discrepancy between this webpage and the UBC Calendar, the UBC Calendar entry will be held to be correct.
Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.
Program Funding Packages
PhD students in the Department of Statistics receive a minimum funding package of $22,000 for the first four years of the program. This funding comes in the form of teaching and/or research assistantships. Motivated students can often find additional sources of funding. Domestic students are expected to apply for NSERC PGSD scholarships.
- 13 students received Teaching Assistantships. Average TA funding based on 13 students was $11,772.
- 17 students received Research/Academic Assistantships. Average RA/AA funding based on 17 students was $13,773.
- 23 students received internal awards. Average internal award funding based on 23 students was $13,051.
- 3 students received external awards. Average external award funding based on 3 students was $31,111.
Scholarships & awards (merit-based funding)
All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.
Teaching Assistantships (GTA)
Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union.
Research Assistantships (GRA)
Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their direction. The duties usually constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is a form of financial support for a period of graduate study and is, therefore, not covered by a collective agreement. Unlike other forms of fellowship support for graduate students, the amount of a GRA is neither fixed nor subject to a university-wide formula. The stipend amounts vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded. Some research projects also require targeted research assistance and thus hire graduate students on an hourly basis.
Financial aid (need-based funding)
Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans.
All students may be able to access private sector or bank loans.
Foreign government scholarships
Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.
Working while studying
The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.
Tax credits and RRSP withdrawals
Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.
Please review Filing taxes in Canada on the student services website for more information.
Applicants have access to the cost calculator to develop a financial plan that takes into account various income sources and expenses.
31 students graduated between 2005 and 2013. Of these, career information was obtained for 29 alumni (based on research conducted between Feb-May 2016):
RI (Research-Intensive) Faculty: typically tenure-track faculty positions (equivalent of the North American Assistant Professor, Associate Professor, and Professor positions) in PhD-granting institutions
TI (Teaching-Intensive) Faculty: typically full-time faculty positions in colleges or in institutions not granting PhDs, and teaching faculty at PhD-granting institutions
Term Faculty: faculty in term appointments (e.g. sessional lecturers, visiting assistant professors, etc.)
Sample Employers in Higher EducationUniversity of British Columbia (3)
Simon Fraser University (3)
Northern Illinois University (2)
University of Dhaka
Grant MacEwan University
University of Toronto
University of Saskatchewan
Ecole des Hautes Etudes Commerciales de Montreal
West Virginia University
Sample Employers Outside Higher EducationGoogle (3)
Children's Hospital of Philadelphia
Eli Lilly and Company
Ghement Statistical Consulting Company Ltd.
Sample Job Titles Outside Higher EducationSenior Statistician (2)
Senior Research Scientist
Senior Statistical Scientist
Staff Data Scientist
PhD Career Outcome SurveyYou may view the full report on career outcomes of UBC PhD graduates on outcomes.grad.ubc.ca.
DisclaimerThese data represent historical employment information and do not guarantee future employment prospects for graduates of this program. They are for informational purposes only. Data were collected through either alumni surveys or internet research.
Enrolment, Duration & Other Stats
These statistics show data for the Doctor of Philosophy in Statistics (PhD). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.
Completion Rates & Times
This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.
Auger-Methe, Marie (Fisheries sciences; Statistics; Zoology; Animal movement; Polar ecology; Statistical Ecology)
Bloem-Reddy, Benjamin (developing methods for evolving networks whose history is unobserved; distributional limits of preferential attachment networks; uses of symmetry in statistics, computation, and machine learning)
Bouchard-Cote, Alexandre (machine/statistical learning; mathematical side of the subject as well as in applications in linguistics and biology)
Campbell, Trevor (automated, scalable Bayesian inference algorithms; Bayesian nonparametrics; streaming data; Bayesian theory; Probabilistic Inference; computational statistics; large-scale data)
Chen, Jiahua (Statistics; asymptotic theory; empirical likelihood; finite mixture model; sample survey)
Cohen Freue, Gabriela (statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity )
Gustafson, Paul (Statistics; meta-analysis; Parametric and Non-Parametric Inference; Theoretical Statistics; Pharmacoepidemiology; Bayesian statistical methods; Biostatistics and Epidemiology; Causal inference; Evidence synthesis; Partial Identification)
Heckman, Nancy (Statistics; Statistics and Probabilities; functional data analysis; smoothing; splines)
Joe, Harry Sue Wah (Statistics; Statistics and Probabilities; copula construction; dependence modelling; extreme value inference; non-normal time series; parsimonous high-dimensional dependence)
Korthauer, Keegan (Bioinformatics; Genomics; Statistics; Epigenomics; Single-cell analysis; Statistical genomics)
McDonald, Daniel (High dimensional data analysis; Computational methods in statistics; Statistical theory and modeling; Machine learning; Estimation and quantification of prediction risk; Evaluating the predictive abilities of complex dependent data; Application of statistical learning techniques to time series prediction problems; Investigations of cross-validation and the bootstrap for risk estimation)
Nolde, Natalia (Statistics; Statistics and Probabilities; Applications in finance, insurance, geosciences; Multivariate extreme value theory; Risk assessment)
Salibian-Barrera, Matias (S-regression estimationg, robust statistics, functional principal component analysis, bootstrap estimators, rgam, clustering algorithm)
Welch, William (, Design of experiments, experiments with computer models, data mining, drug discovery, quality improvement)
Wu, Lang (Biostatistics)
Zamar, Ruben (Data mining and text mining, Modeling data quality, Development of new robust procedures, Statistical computing, Bioinformatics)
|2020||Dr. Kepplinger devised reliable statistical methods to identify proteins for predicting severity of heart diseases in the presence of anomalous protein levels, an issue as technology affords measuring numerous proteins. Beyond proteomics, these statistical methods boost generalizability of results from studies with few subjects but many variables.|
|2019||Dr. Chang studied vine copulas, a hierarchical graphic tool used in statistics and probability distributions. He found that vine copulas relax the restrictive assumptions in classical multivariate Gaussian elliptical dependence. This work can be applied to machine learning and used in real-world data sets such as stock indices and weather.|
|2019||Dr. Campbell examined how publication policy impacts the reliability of scientific research from a statistical perspective. He proposed novel policy prescriptions and modelled how adopting these could transform the incentives driving research. This work aims to address the reproducibility crisis and issues of publication bias.|
|2019||Dr. Zhao worked on improving probabilistic models for Continuous Time Markov Chains and developing Bayesian models and associated Monte Carlo methods for inference. Her modelling framework has been applied to build novel protein evolution models, where the model complexity can be controlled and good estimation is achieved.|
|2019||Dr. Yu developed statistical models and methods that can assess associations between longitudinal data and survival data, and handle the complications in the longitudinal data simultaneously. She applied her methods to an HIV vaccine study and discovered significant relationships between the risk of HIV infection and some immune response biomarkers.|
|2019||Researchers today are able to study the behaviour of deep diving animals via sensors that generate high volumes of data. Dr. Fu developed automatic data analytic methods to group dive depth trajectories of southern elephant seals by dive shape. His methods help researchers understand seals' foraging and resting behaviour.|
|2018||Dr. Dinsdale developed new statistical methods to improve the prediction of oceanographic measurements, for example water temperature, using data collected by tags attached to marine mammals such as seals. This research helps to improve our understanding of changing ocean dynamics in sparsely sampled areas such as near Antarctica.|
|2018||Developing a new drug can be a complicated, time consuming and expensive process. Dr. Yu developed a new optimal design method, which will accurately estimate the safe and effective dose level of the new drug for patients. Her study greatly improves the accuracy and safety of clinical trials, and speeds up the drug development process.|
|2018||Many practical problems are subject to order constraints, for example, combined physical and chemical therapies are usually at least as good as chemical therapy alone. Dr. Zhou developed methods to formally utilize order constraints for statistical inference. His methods enable scientists from various disciplines to make more efficient use of the available data resources.|
|2018||Dr. Chen examined both the design and analysis of computer experiments from a statistical perspective. He developed a new method to estimate the unknown parameters of a Gaussian process model. He also assessed the performance of some existing methods in sequential experimental design and provided insights into issues faced by practitioners.|
Sample Thesis Submissions
Research interests of the faculty include biostatistics, environmetrics, mathematical modelling of biological systems, computational statistics, data mining, machine learning, theory of statistical inference, asymptotics, multivariate analysis, robustness, nonparametrics, design of experiments, smoothing, Bayesian methods, computational molecular biology, gene expression, and microarrays.