Doctor of Philosophy in Statistics (PhD)

Overview

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.

 

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Admission Information & Requirements

In order to apply to this program, the following components may be required.

Online Application

All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.

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. Meeting the minimum requirements does not guarantee admission as it is a competitve process.

Transcripts

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.

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:

100
22
21
22
21
7.5
6.5
6.5
6.5
6.5

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.

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.

Supervision

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)
The program will review research interests of applicants and recommend/match faculty members during the application/evaluation process. Applicants should not reach out to faculty members directly.

Document Requirements

We require a 2 page (maximum) statement of interest/research proposal, as well as a CV.

Prerequisites / Course 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).

Citizenship Verification

Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.

Research Information

Research Focus

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

Program Components

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

Tuition

FeesCanadian Citizen / Permanent Resident / Refugee / DiplomatInternational
Application Fee$106.00$168.25
Tuition *
Installments per year33
Tuition per installment$1,698.56$2,984.09
Tuition per year
(plus annual increase, usually 2%-5%)
$5,095.68$8,952.27
Int. Tuition Award (ITA) per year (if eligible) $3,200.00 (-)
Other Fees and Costs
Student Fees (yearly)$944.51 (approx.)
Costs of living (yearly)starting at $16,954.00 (check cost calculator)
* Regular, full-time tuition. For on-leave, extension, continuing or part time (if applicable) fees see UBC Calendar.
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.

Financial Support

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 $18,000 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. 

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.

International students enrolled as full-time students with a valid study permit can work on campus for unlimited hours and work off-campus for no more than 20 hours a week.

A good starting point to explore student jobs is the UBC Work Learn program or a Co-Op placement.

Tax credits and RRSP withdrawals

Students with taxable income in Canada may be able to claim federal or provincial tax credits.

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.

Cost Calculator

Applicants have access to the cost calculator to develop a financial plan that takes into account various income sources and expenses.

Career Outcomes

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 Education
University of British Columbia (3)
Simon Fraser University (3)
Northern Illinois University (2)
University of Dhaka
Grant MacEwan University
University of Toronto
University of Saskatchewan
York University
Ecole des Hautes Etudes Commerciales de Montreal
West Virginia University
Sample Employers Outside Higher Education
Google (3)
Scotiabank
TransUnion
eBay
Genentech
AstraZeneca
Children's Hospital of Philadelphia
Eli Lilly and Company
Ghement Statistical Consulting Company Ltd.
Sample Job Titles Outside Higher Education
Senior Statistician (2)
Statistician
Senior Research Scientist
Data Scientist
Senior Consultant
Senior Statistical Scientist
Principal
Director Risk
Staff Data Scientist
Quantitative Analyst
PhD Career Outcome Survey
You may view the full report on career outcomes of UBC PhD graduates on outcomes.grad.ubc.ca.
Disclaimer
These 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.
Career Options

Our students are prepared for a successful career in industry, academia or the public sector. Former students looking for a job after graduation have been promptly offered employment in many different industries, universities and government agencies. Please view a list of alumni and their first positions after graduation on our website.

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.

Enrolment Data

 20192018201720162015
Applications5970403833
Offers119495
New registrations66453
Total enrolment2727242725

Completion Rates & Times

This program has a graduation rate of 87.5% based on 16 students admitted between 2006 - 2009. Based on 16 graduations between 2015 - 2018 the minimum time to completion is 4.32 years and the maximum time is 7.00 years with an average of 5.02 years of study. All calculations exclude leave times.
Disclaimer
Admissions data refer to all UBC Vancouver applications, offers, new registrants for each year, May to April [data updated: 10 March 2020]. Enrolment data are based on March 1 snapshots. Program completion data are only provided for datasets comprised of more than 4 individuals. Rates and times of completion depend on a number of variables (e.g. curriculum requirements, student funding), some of which may have changed in recent years for some programs [data updated: 27 October 2019].

Research Supervisors

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 (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 (finite mixture model, empirical likelihood, asymptotic theory, sample survey)
  • Cohen Freue, Gabriela (statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity )
  • Gustafson, Paul (Meta-Analysis, Parametric and Non-Parametric Inference, Theoretical Statistics, Pharmacoepidemiology, Bayesian statistical methods, Causal inference, Evidence synthesis, Biostatistics and Epidemiology, Partial Identification)
  • Heckman, Nancy (Statistics and Probabilities, functional data analysis, smoothing, splines)
  • Joe, Harry Sue Wah (Statistics and Probabilities, dependence modelling, copula construction, non-normal time series, extreme value inference, parsimonous high-dimensional dependence)
  • Korthauer, Keegan (Statistical genomics, Epigenomics, Single-cell analysis)
  • Mostafavi, Sara (machine/statistical learning applied to disease genetics )
  • Nolde, Natalia (Statistics and Probabilities, Multivariate extreme value theory, Risk assessment, Applications in finance, insurance, geosciences)
  • 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)

Doctoral Citations

A doctoral citation summarizes the nature of the independent research, provides a high-level overview of the study, states the significance of the work and says who will benefit from the findings in clear, non-specialized language, so that members of a lay audience will understand it.
Year Citation
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.
2017 Using machine learning techniques, Dr. Zhang developed a method to group high-dimensional cases using hierarchical approaches. He also developed a predictive framework called Regression Phalanxes which selects subsets of features that work well together for prediction. This new framework outperforms current methods in a variety of applications.

Pages

Further Program Information

Specialization

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.

Faculty Overview

Academic Unit

Program Identifier

VGDPHD-XA
 
 
 

Supervisor Search

 

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