Preliminary Fall / Winter 2021-2022 Timetable

If you are planning to enrol in a graduate course at the Department of Statistical Sciences, we recommend you read through this page carefully. Please also note, that this course schedule is subject to changes. We will post changes and updates here, so check back frequently.

Enrolment Dates

Students enrolled at our department

Registration for the Fall Session starts at 6:00 am EST on Tuesday, July 20, 2021 for students enroled at the Department of Statistical Sciences only.

Students from other departments

Course enrolment for students from other department will start on Tuesday, August 24, 2021. You will not be able to add courses before this date. To enrol in one of our graduate courses, you will have to...

For enrolment into courses STA1001H – STA1004H,  please submit your request here before sending your course add/drop form to the instructor.

If you have any additional questions, please contact the graduate team.

Start and End Dates of Classes & Final Examination Period

Term Classes Start Classes End

Fall Session 2021
(cross-listed courses)

Thursday, September 9, 2021 Wednesday, December 8, 2021 (Dec 9, 2021 Make-up Day)

Fall Session 2021
(graduate courses, not cross-listed)

Thursday, September 9, 2021 Wednesday, December 8, 2021 (Dec 9, 2021 Make-up Day)

Winter Session 2021
(all courses)

Monday, January 10, 2022

Friday, April 8, 2022

 

At this point in time, the Faculty of Arts & Science and the University of Toronto are optimistic that most courses, student services and co-curricular activities will be able to proceed in person, with the possible exception of large-scale gatherings. Please see UTogether for more information on the University of Toronto’s plans for Fall 2021.

For dates regarding university closures, course drop and registration deadlines, and tuition payment deadlines, please have a look at the School of Graduate Studies sessional dates calendar.

Course List Legend

  • F = a half-year course in the first term (September – December)
  • S = a half-year course in the second term (January– April)
  • Y = a full-year course (September – April)
  • (J) indicates a a cross-listed course (a joint graduate/undergraduate course)
  • M = Monday
  • T = Tuseday
  • W = Wednesday
  • R = Thursday
  • F = Friday
  • L0101 or L0201 = 9:00 am to 5:00 pm
  • L5101 = 5:00 pm onwards

Fall / Winter 2021-22 Timetable Course Listings

The Fall / Winter 2021-22 Timetable consists of two sessions: the Fall Session 2021 and the Winter Session 2022. Please find course listings for both sessions below. You can also find a list of our graduate courses at the School of Graduate Studies page for our department.

 

 

Fall Session 2021: Course Listings

Course Title (Click for description) Session Section/Time Location Instructor Delivery Method
STA1001H (STA302H1)

(also offered as undergraduate course STA302H1)

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Prerequisite:

  • STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1
  • CSC108H1/CSC120H1/CSC121H1/CSC148H1
  • MAT221H1(70%)/MAT223H1/MAT240H1
F L5101: W6-9 Online

Daignault, Katherine

Online
STA1003H (STA304H1)

(also offered as undergraduate course STA304H1)

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias.

Exclusion: STA322H1

Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1

F
L0201: LEC W2-4, TUT W4
Online Caetano, Samantha-Jo Online
STA1004H (STA305H1)
(also offered as undergraduate course STA305H1)
 

This cross-listed course covers a number of topics used in the design and analysis of experiments. The course is intended for students of statistics as well as students of other disciplines (eg. engineering, experimental science, etc.) who will use experimental design and analysis in their work.

The course will cover the following topics: randomization, blocking Latin squares, balanced incomplete block designs, factorial experiments, confounding and fractional replication, components of variance, orthogonal polynomials, response surface methods. Additional topics will be covered based on students’ interest as time permits.

Prerequisite: STA302H/352Y/ECO327Y/ECO357Y or permission of instructor.

F L0101: W1; F1-3  TBA Singh, Murari In-person

STA1008H

Vocabulary of data analysis, Tests of statistical significance, Principles of research design, Introduction to unix, Introduction to SAS, Elementary significance tests, Multiple regression, Factorial ANOVA, Permutation tests, Power and sample size, Random effects models, Multivariate analysis of variance, Analysis of within-cases designs (repeated measures). If time permits, Categorical data analysis.

Prerequisite: Any introductory statistics class, taught by any department.

F
L0101: T10-12; F10
TBA In-person
STA2005H (STA437H1)

(also offered as undergraduate course STA437H1)

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical cor relations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected.

Prerequisite: STA302H/352Y

Recommended Preparation: MAT223H/240H

F L0101: W6-9 TBA Molkaraie, Mehdi In-person

STA2047H

A rigorous introduction to stochastic analysis and its applications. Topics include Brownian motion, continuous time martingale, stochastic integration, stochastic differential equations, diffusions, and further topics depending on the interests of the instructor.

Prerequisite: No explicit prerequisites, but to understand the material, it is necessary to have a good understanding of real analysis and probability theory at the graduate level.

Course credit: 0.5 FCE

F
L0101: M10-12; W11
 TBA

Wong, Leonard

In-person

STA2052H

Modern statistical methods and data analytics are increasingly informing decisions in law, business, medicine, and public life. While the use of statistics to understand social problems is not new, its pervasiveness in society and the scale of available data available opens up a host of new and/or salient moral problems including, for example, fairness, bias, privacy, equality, transparency, accountability, and accessibility.

In this course, we will combine material from law and philosophy together with recent work in statistics and data science in order to gain a better understanding of how to intelligibly reason about these problems, and how to responsibly and creatively apply statistical methods to complex social problems.

The course will be research/project based and the emphasis will be on using statistics to address complex social problems rather than on memorizing abstract ethical principles for handling or processing data.

Prerequisite: Graduate students should have an adequate background in probability and statistics (including the use of R), equivalent to two undergraduate courses in the field. Familiarity with Bayesian approaches and statistical learning/classification would also be helpful. No formal prerequisites.

Course credit: 0.5 FCE

F

L5101: W6-9

TBA

Babic, Boris

In-person

STA2101H

This course will focus on principles and methods of applied statistical science. It is designed for MSc and PhD students in Statistics, and is required for the Applied Paper of the PhD comprehensive exams.  The topics covered include: planning of studies, review of linear models, analysis of random and mixed effects models, model building and model selection, theory and methods for generalized linear models, and an introduction to nonparametric regression. Additional topics will be introduced as needed in the context of case studies in data analysis.

Prerequisite:

  • ECO374H1/ECO375H1/STA302H1
  • STA305H1
F

L0101: W10-1

TBA Reid, Nancy In-person

STA2111H

STA 2111H is a course designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: probability measures, the extension theorem, random variables, distributions, expectations, laws of large numbers, Markov chains.

Students should have a strong undergraduate background in Real Analysis, including calculus, sequences and series, elementary set theory, and epsilon-delta proofs. Some previous exposure to undergraduate-level probability theory is also recommended.

F L0101: R10-1 TBA Zhou, Zhou In-person

STA2112H

This course is designed for graduate students in Statistics and Biostatistics.

Review of probability theory, distribution theory for normal samples, convergence of random variables, statistical models, sufficiency and ancillarity, statistical functionals,influence curves, maximum likelihood estimation, computational methods.

Prerequisite:

  • Advanced calculus (eg. MAT237)
  • Linear algebra (eg. MAT223, MAT224).
  • A previous course in probability and/or statistics is highly recommended.
F L0101: T10-1

TBA

Gronsbell, Jessica In-person

STA2163H

This course presents mathematical foundations for learning, prediction, and decision making. Unlike in traditional statistical learning, however, our focus will be on notions of optimality that do not rely on stochastic modeling assumptions on data. A primary focus will be on learning from data to compete with a class of baselines predictors / strategies, often referred to as experts. A secondary focus will be on the ability to adapt to the presence or absence of statistical patterns, without presuming at the outset that such patterns will arise. Topics include: regret; prediction with expert advice; the role of the loss function in tight bounds; online classification; online linear and convex optimization; regularization; bandit problems / decisions with limited feedback; minimax optimality and adaptivity; relationships with statistical learning.

Prerequisite: Mathematical maturity, including real analysis, linear algebra, and probability theory.

Course credit: 0.5 FCE

F
L0101: M11-2
TBA In-person
STA2202H (STA457H1)

(also offered as undergraduate course STA457H1)

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models.

The course will cover the following topics:

  • Theory of stationary processes, linear processes
  • Elements of inference in time domain with applications
  • Spectral representation of stationary processes
  • Elements of inference in frequency domain with applications
  • Theory of prediction (forecasting) with applications > ARMA processes, inference and forecasting
  • Non-stationarity and seasonality, ARIMA and SARIMA processes

Further topics, time permitting: multivariate models; GARCH models; state-space models

F L5101: LEC T6-8, TUT T8 Online Nadarajah, Tharshanna Online

STA2453H

This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (September-December) and winter (January-April). The overall workload is approximately equivalent to a half graduate course and students receive a half credit.

Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants.

Content: There is some classroom instruction at the start of the term, an d meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times.

Evaluation: Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients.

Prerequisite: Students should have taken some applied sta tistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work.

Y

L0101: T10-12

TBA

Murray, Josh In-person
STA2500H (ACT451H1)

Parametric distributions and transformations, insurance coverage modifications, limits and deductibles, models for claim frequency and severity, models for aggregate claims,stop-loss insurance, risk measures.

Prerequisite: Consult the instructor concerning necessary background for this course.

F L0101: T11, R10-12 TBA Lin, Sheldon In-person
STA2501H

Consult the instructor for further details.

Prerequisite: Consult the instructor concerning necessary background for this course.

F L0101: M9-12 TBA Lin, Sheldon In-person

STA2502H (ACT460H1)

(also offered as undergraduate course ACT460H1)

This course is an introduction to the stochastic models used in Finance and Actuarial Science. Students will be exposed to the basics of stochastic calculus, particularly focusing on Brownian motions and simple stochastic differential equations. The role that martingales play in the pricing of derivative instruments will be investigated. Some exotic equity derivative products will be explored together with stochastic models for interest rates.

Prerequisite:

  • Knowledge of undergraduate probability theory is necessary.
  • Knowledge of basic financial modeling (e.g., binomial trees and log-normal distributions) is useful, but not completely necessary.
F L0101: T2-5 TBA Zhang, Yuchong In-person
STA2503H

This course features studies in derivative pricing theory and focuses on financial mathematics and its applications to various derivative products. A working knowledge of probability theory, stochastic calculus (see e.g., STA 2502), knowledge of ordinary and partial differential equations and familiarity with the basic financial instruments is assumed.

The tentative topics covered in this course include, but is not limited to:

  • no-arbitrage and the fundamental theorem of asset pricing,
  • binomial pricing models;
  • continuous time limits;
  • the Black-Scholes model;
  • the Greeks and hedging;
  • European, American, Asian, barrier and other path-dependent options;
  • short rate models and interest rate derivatives;
  • convertible bonds;
  • stochastic volatility and jumps;
  • volatility derivatives;
  • foreign exchange and commodity derivatives.

More information: Course Website STA 2503.

Prerequisite:  Knowledge of undergraduate probability theory is necessary. Knowledge of basic financial modeling (e.g., binomial trees and log-normal distributions), introductory stochastic calculus and financial products is useful, but not necessary. This course moves at a faster pace, is more advanced and contains a higher workload than STA2502, only students who are well prepared will be allowed to take this course. It is also distinct from STA 2047 which instead focuses on the mathematics of stochastic analysis.  This course requires instructor approval prior to enrolment.

F L0101: LEC W2-5, TUT M4-6 TBA Jaimungal, Sebastian SYNC
(CSC2537H)

In this course we will study techniques and algorithms for creating effective data visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science.This course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

F L9101: W2-4 TBA Chevalier, Fanny SYNC

STA2700H

This is a reading course primarily meant to sequentially follow a modular course offered in the Department. Its purpose is to offer further supervised study of an advanced topic covered for the ambitious student.

F L0101: T9-11; R11 TBA Molkaraie, Mehdi In-person

STA3000Y

Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences.

This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings.

Prerequisite:

  • STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be co-requisites).
  • Some familiarity with measure theory is very useful. The text includes some supplementary material on this.
Y L0101: T10-1 TBA Wang, Linbo/Kong, Dehan In-person

STA3431H

This course will explore Monte Carlo computer algorithms, which use randomness to perform difficult high-dimensional computations. Different types of algorithms, theoretical issues, and practical applications will all be considered. Particular emphasis will be placed on Markov chain Monte Carlo (MCMC) methods. The course will involve a combination of methodological investigations, mathematical analysis, and computer programming.

Prerequisite: Knowledge of statistical inference and probability theory at the advanced undergraduate level, and familiarity with basic computer programming techniques.

F L0101: M10-12 TBA Rosenthal, Jeffrey In-person
STA4526H

The course will introduce students to the basic theory of stochastic optimal control. We will cover both the analytic approach, including an introduction to viscosity solution theory, and the probabilistic approach which is based on BSDE and the stochastic maximum principle. Applications to portfolio optimization and contract theory will be discussed. Prerequisite to this course include (measure-theoretic) probability theory and stochastic calculus.

Prerequisite: (measure-theoretic) probability theory and stochastic calculus

Course credit: 0.25 FCE

F (First Half of Semester) L0101: M1-4 TBA Zhang, Yuchong In-person

 

Winter Session 2022: Course Listings

Course Title (Click for description) Session Section/Time Location Instructor Delivery Method
STA1001H (STA302H1)

(also offered as undergraduate course STA302H1)

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Prerequisite:

  • STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1
  • CSC108H1/CSC120H1/CSC121H1/CSC148H1
  • MAT221H1(70%)/MAT223H1/MAT240H1
S L0101: W3-6 Online Daignault, Katherine Online
STA1002H (STA303H1)

also offered as undergraduate course STA303H1)

Analysis of variance for one-and two-way layouts, logistic regression, loglinear models, longitudinal data, introduction to time series.

Prerequisite: STA1001H or equivalent

S
L0201: LEC W3-5, TUT R5
Online Bolton, Liza Online
STA1003H (STA304H1)

(also offered as undergraduate course STA304H1)

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias.

Exclusion: STA322H1

Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1

S L0101: M3; W3-5 TBA Alexander, Rohan In-person
STA1004H (STA305H1)

(also offered as undergraduate course STA305H1)

This cross-listed course covers a number of topics used in the design and analysis of experiments. The course is intended for students of statistics as well as students of other disciplines (eg. engineering, experimental science, etc.) who will use experimental design and analysis in their work.

The course will cover the following topics: randomization, blocking Latin squares, balanced incomplete block designs, factorial experiments, confounding and fractional replication, components of variance, orthogonal polynomials, response surface methods. Additional topics will be covered based on students’ interest as time permits.

Prerequisite: STA302H/352Y/ECO327Y/ECO357Y or permission of instructor

S
L0201: T3; R3-5
TBA Singh, Murari In-person
STA2005H (STA437H1)

(also offered as undergraduate course STA437H1)

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical cor relations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected.

Prerequisite: STA302H/352Y

Recommended Preparation: MAT223H/240H

S
L0101: T2-5
L0201: R3-6
TBA Zhou, Zhou In-person
STA2006H (STA447H1)

(also offered as undergraduate course STA447H1)

Discrete and continuous time processes with an emphasis on Markov, Gaussian and renewal processes. Martingales and further limit theorems. A variety of applications taken from some of the following areas are discussed in the context of stochastic modeling: Information Theory, Quantum Mechanics, Statistical Analyses of Stochastic Processes, Population Growth Models, Reliability, Queuing Models, Stochastic Calculus, Simulation (Monte Carlo Methods).

Prerequisite: STA347H or equivalent knowledge of probability theory; and MAT235Y/237Y or equivalent knowledge of multivariate calculus and basic real analysis.

S L5101: R6-9 TBA Park, Jun Young In-person

Consult the instructor for further details.

Prerequisite: Consult the instructor concerning necessary background for this course.

S L0101: W2-4 TBA UTSC Butler, Kenneth In-person
STA2016H (STA465H1)

(also offered as undergraduate course STA465H1)

Data acquisition trends in the environmental, physical and health sciences are increasingly spatial in character and novel in the sense that modern sophisticated methods are required for analysis. This course will cover different types of random spatial processes and how to incorporate them into mixed effects models for Normal and non-Normal data. Students will be trained in a variety of advanced techniques for analyzing complex spatial data and, upon completion, will be able to undertake a variety of analyses on spatially dependent data, understand which methods are appropriate for various research questions, and interpret and convey results in the light of the original questions posed.

S L0101: W12; F11-1 TBA Leos-Barajas, Vianey In-person
STA2051H

Techniques for formulating data science models as optimization problems. Algorithms for solving data science problems including gradient-descent based algorithms and randomized algorithms.  Emphasis on scalability and efficiency. Convergence analysis of algorithms.  Coverage of both convex and nonconvex optimization.

Course credit: 0.5 FCE

S L0101: T2-4; R2 TBA TBA In-person
STA2102H (STA410H1)

(also offered as undergraduate course STA410H1)

The goal of this course is to give an overview of some of the computational methods that are useful in statistics. The rst part of the course will focus on basic algorithms, such as the Fast Fourier Transform (and related methods) and methods for generating random variables. The second part of the course will focus on numerical methods for linear algebra and optimization (for example, computing least squares estimates and maximum likelihood estimates). Along the way, you will learn some basic theory of numerical analysis (computational complexity, convergence rates of algorithms) and you will encounter some statistical methodology that you may not have seen in other courses.

Prerequisites: The nominal prerequisites for this course are MAT223H/240H, STA302H and CSC108H/120H/121H/148H these should give you the sucient background in both statistics and computer programming to handle the course material. A solid foundation in linear algebra is very useful for this course.

S
L0101: T3-6
L5101: W6-9
TBA Schwartz, Scott In-person
STA2104H (STA414H1)

(also offered as undergraduate course STA414H1)

This course will consider topics in statistics that have played a role in the development of techniques for data mining and machine learning. We will cover linear methods for regression and classification, nonparametric regression and classification methods, generalized additive models, aspects of model inference and model selection, model averaging and tree bassed methods.

Prerequisite: either STA302H or CSC411H

S
L0101: M2-5
L5101: T6-9
TBA
TBA
In-person

STA2201H

The course will focus on generalized linear models (GLM) and related methods, such as generalized additive model involving nonparametric regression, generalized estimating equations (GEE) and generalized linear mixed models (GLMM) for longitudinal data. This course is designed for Master and PhD students in Statistics, and is REQUIRED for the Applied paper of the PhD Comprehensive Exams in Statistics. We deal with a class of statistical models that generalizes classical linear models to include many other models that have been found useful in statistical analysis, especially in biomedical applications. The course is a mixture of theory and applications and includes computer projects featuring R (S+) or/and SAS programming.

Topics: Brief review of likelihood theory, fundamental theory of generalized linear models, iterated weighted least squares, binary data and logistic regression, epidemiological study designs, counts data and log-linear models, models with constant coefficient of variation, quasi-likelihood, generalized additive models involving nonparametric smoothing, generalized estimating equations (GEE) and generalized linear mixed models (GLMM) for longitudinal data.

Prerequisite: Advanced Calculus, Linear Algebra, STA 347 and STA 422 (upper-division courses on probability and statistical inference) or equivalent, STA 302 (linear regression), Statistical Computing using R (S+) or/and SAS (alternative softwares are allowed). However, please be advised that I may not be familiar with the software of your choice resulting in limited assistance.

S L0101: W10-1 TBA In-person
STA2202H (STA457H1)

(also offered as undergraduate course STA457H1)

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models.

The course will cover the following topics:

  • Theory of stationary processes, linear processes
  • Elements of inference in time domain with applications
  • Spectral representation of stationary processes
  • Elements of inference in frequency domain with applications
  • Theory of prediction (forecasting) with applications > ARMA processes, inference and forecasting
  • Non-stationarity and seasonality, ARIMA and SARIMA processes

Further topics, time permitting: multivariate models; GARCH models; state-space models

S
L0101: LEC R3-5, TUT R5 
L5101: LEC T6-8, TUT T8
Online Nadarajah, Tharshanna Online

STA2211H

STA 2211H is a follow-up course to STA 2111F, designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: weak convergence, characteristic functions, central limit theorems, the Radon-Nykodym Theorem, Lebesgue Decomposition, conditional probability and expectation, martingales, and Kolmogorov’s Existence Theorem.

S L0101: R10-1 TBA Volgushev, Stanislav In-person

STA2212H

This course is a continuation of STA2112H. It is designed for graduate students in statistics and biostatistics.

Topics include:

  • Likelihood inference
  • Bayesian methods
  • Significance testing
  • Linear and generalized linear models
  • Goodness-of-fit
  • Computational methods

Prerequisite: STA2112H

S L0101: T10-1 TBA Reid, Nancy In-person

STA2453H

This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (September-December) and winter (January-April). The overall workload is approximately equivalent to a half graduate course and students receive a half credit.

Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants.

Content: There is some classroom instruction at the start of the term, an d meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times.

Evaluation: Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients.

Prerequisite: Students should have taken some applied sta tistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work.

Y L0101: T10-12 TBA Murray, Josh In-person
STA2505H (ACT466H1)

(also offered as undergraduate course ACT466H1)

Limited fluctuation or American credibility, on a full and partial basis. Greatest accuracy or European credibility, predictive distributions and the Bayesian premium, credibility premiums including the Buhlmann and Buhlmann-Straub models, empirical Bayes nonparametric and semi-parametric parameter estimation. Simulation, random numbers, discrete and continuous random variable generation, discrete event simulation, statistical analysis of simulated data and validation techniques.

Prerequisite: Consult the instructor concerning necessary background for this course.

S L0101: T11, R10-12 TBA Badescu, Andrei In-person

STA3000Y

Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences.

This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings.

Prerequisite:

  • STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be co-requisites).
  • Some familiarity with measure theory is very useful. The text includes some supplementary material on this.
Y L0101: W10-1 TBA Zwiernik, Piotr In-person
STA4246H

This course focuses on advanced theory and modeling of financial derivatives. The topics include, but are not limited to: HJM interest rate models, LFM and LSM market models; foreign exchange options; defaultable bonds; credit default swaps, equity default swaps and collateralized debt obligations; intensity and structural based models; jump processes and stochastic volatility; commodity models. As well, students are required to complete a project, write a report and present a topic of current research interest.

Prerequisite: STA 2503 or equivalent knowledge.

S L5101: M6-9 TBA Zhang, Yuchong In-person
STA4506H

The course will cover modeling, estimation and inference of non-stationary time series. In particular, we will deal with statistical inference of trends, quantile curves, time-varying spectra and functional linear models related to non-stationary time series. With the recent advances in various fields, a systematic account of non-stationary time series analysis is needed.

Course credit: 0.25 FCE

S (Second Half of Semester) L0101: R10-1 TBA Zhou, Zhou In-person
STA4508H

Inference based on the likelihood function has a prominent role in both theoretical and applied statistics.  This course will introduce some of the more recent developments in likelihood-based inference, with an emphasis on adaptations developed for models with complex structure or large numbers of nuisance parameters.  Special emphasis will be given to the theoretical and applied aspects of composite likelihood, and to the use of quasi-likelihood and generalized estimating equations. Tentative topics to be covered include: review of likelihood inference and asymptotic results; adjustments to profile likelihood; misspecified models — composite likelihood; partially specified models — quasi-likelihood; properties and limitations of penalized likelihood.

Course credit: 0.25 FCE

S (First Half of Semester) L0101: W2-5 TBA Reid, Nancy In-person
STA4512H

The general mathematics and logical foundations for statistical inference: geometric, algebraic and topological symmetries that arise naturally in the solution to the inference problem, including rigorous comparison of the bayesian and frequentist approaches, and the group theoretic considerations of invariance (algebraic and logical symmetry), both on the sample space as well as on the parameter space (and both either implicit or manifest) that must be taken into account in the analysis. Unusual for the development, but fundamental to the inherent logic of such considerations, the finite-finite case is given special attention in respect of both sample space and parameter space.

Course credit: 0.25 FCE

S (Second Half of Semester) L0101: M12-3 TBA Brenner, David In-person
STA4518H

This course will give an overview of robust statistical methods, that is, methods that are insensitive to outliers or other data contamination. Topics will include theoretical notions such as qualitative robustness and breakdown point, robust estimation of location (minimax variance and bias) and scale parameters, robust estimation in regression and multivariate analysis, and applications (including in computer vision).

Prerequisite:

  •     STA2112H
  •     permission

Course credit: 0.25 FCE

S (Second Half of Semester) L0101: T1-4 TBA Knight, Keith In-person

JAS1101H

This graduate-level course provides an introduction to the cross-disciplinary field of astrostatistics, and is intended for both astronomy and statistics students. We will cover topics in statistics (e.g., hierarchical Bayesian analysis, time series analysis, and cluster analysis) in the context of their applications to astronomical research (e.g., studies of galaxies, the Milky Way, exoplanets, and stellar populations).

These topics will be covered through two main aspects of the course: 1) peer-instruction and collaboration on a term project, and 2) readings, in-class discussion, and exercises related to current astrostats literature. For the term project, the students will develop practical skills by collaborating in cross-disciplinary teams on a research project in astrostatistics using real astronomical data.

S

L0101: T1-2:30; W3-4:30

TBA Eadie, Gwendolyn In-person