Search Course Outline
Showing 25 course outlines from 2938 matches
2251
STATS 723
: Stochastic Methods in Finance2021 Semester One (1213)
Contingent claims theory in discrete and continuous time. Risk-neutral option pricing, Cox-Ross-Rubinstein and Black-Scholes models, stochastic calculus, hedging and risk management.
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2252
STATS 723
: Stochastic Methods in Finance2020 Semester One (1203)
Contingent claims theory in discrete and continuous time. Risk-neutral option pricing, Cox-Ross-Rubinstein and Black-Scholes models, stochastic calculus, hedging and risk management.
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2253
STATS 726
: Time Series2023 Semester Two (1235)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
2254
STATS 726
: Time Series2022 Semester Two (1225)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
2255
STATS 726
: Time Series2021 Semester Two (1215)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
2256
STATS 726
: Time Series2020 Semester Two (1205)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
No pre-requisites or restrictions
2257
STATS 727
: Foundations of Applied Time Series Analysis2021 Semester One (1213)
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208, 707
Restriction: STATS 326
Restriction: STATS 326
2258
STATS 727
: Foundations of Applied Time Series Analysis2020 Semester One (1203)
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208
Restriction: STATS 326
Restriction: STATS 326
2259
STATS 730
: Statistical Inference2023 Semester Two (1235)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Prerequisite: STATS 310 or 732
2260
STATS 730
: Statistical Inference2022 Semester Two (1225)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Prerequisite: STATS 310 or 732
2261
STATS 730
: Statistical Inference2021 Semester Two (1215)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Prerequisite: STATS 310 or 732
2262
STATS 730
: Statistical Inference2020 Semester Two (1205)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Prerequisite: STATS 310 or 732
2263
STATS 731
: Bayesian Inference2023 Semester Two (1235)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
2264
STATS 731
: Bayesian Inference2022 Semester Two (1225)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 210 or 225
2265
STATS 731
: Bayesian Inference2021 Semester Two (1215)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 210 or 225
2266
STATS 731
: Bayesian Inference2020 Semester One (1203)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 210 or 225
2267
STATS 732
: Foundations of Statistical Inference2023 Semester One (1233)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2268
STATS 732
: Foundations of Statistical Inference2022 Semester One (1223)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2269
STATS 732
: Foundations of Statistical Inference2021 Semester One (1213)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2270
STATS 732
: Foundations of Statistical Inference2020 Semester One (1203)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2271
STATS 740
: Sample Surveys2022 Semester Two (1225)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2272
STATS 740
: Sample Surveys2021 Semester Two (1215)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2273
STATS 740
: Sample Surveys2020 Semester Two (1205)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Prerequisite: 15 points from STATS 340, 741 and 15 points from STATS 310, 732
2274
STATS 747
: Statistical Methods in Marketing2021 Semester Two (1215)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2275
STATS 747
: Statistical Methods in Marketing2020 Semester Two (1205)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
No pre-requisites or restrictions
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