## MATH 3460 - Winter 2014

#### ## Intermediate Statistical Theory This is the page where I post material related to the MATH/STAT 3460 course I am teaching in the Winter term 2014.

• Office hours: Monday 15:30-16:30, Wednesday 15:30-16:30, Thursday 10:00-11:00
• Office: 251 Chase building (on the 2.5th floor - you need to take the stairs at the South end of the building.)
• If you want to come to my office at a different time please email me:tkenney@mathstat.dal.ca
• Midterm Exam: Monday 3rd March, in class.
• Here are some practice questions for the midterm examination. Here are the solutions.
• Here is the midterm examination. Here are the solutions.
• Textbook: Probability and Statistical Inference Volume 2: Statistical Inference (Second Edition) by J. G. Kalbfleisch, published by Springer-Verlag, 1985
• Final Exam: Wednesday April 16th, 19:00-22:00, Dalplex.
• Here are some practice questions for the final examination. Here are the solutions.
• Here is the formula sheet for the final examination.

#### Handouts

Course Handout

#### Planned material

Lecture time is limited, so I plan to use it explaining concepts and giving examples, rather than reading the textbook. Therefore, to get the most out of each lecture, you should read the relevant material before the lecture. Here is the list of what I expect to cover in each lecture. This is subject to change - make sure to check regularly for changes.

Here are some questions on these topics that we may go over in class.

Week beginning Monday Wednesday Friday
6th January Introduction 9 Maximum Likelihood
• 9.1 Maximum Likelihood
• 9.1 Maximum Likelihood(Cont.)
• 13th January
• 9.2 Combining Independent Events
• 9.3 Relative Likelihood
• 9.4 Likelihood for Continuous Models
• 9.5 Censoring in Lifetime Experiments
• 9.6 Invariance
• 20th January
• 9.6 Invariance (cont.)
• 9.7 Normal Approximations
• Snow Day
• 9.7 Normal Approximations (cont.)
• 9.8 Newton's Method
• 27th January 10 Two parameter Maximum Likelihood
• 10.1 Two-Parameter Maximum Likelihood Estimation
• 10.2 Relative Likelihood and Contour Maps
• 10.3 Maximum Relative Likelihood
• 10.4 Normal Approximations
• 10.4 Normal Approximations (cont.)
• 10.5 A Dose-Response Example
• 3rd February
• 10.6 An Example from Learning Theory
• 11 Frequency Properties
• 11.1 Sampling Distributions
• 11.2 Coverage Probability
• Munro Day
10th February
• 11.3 Chi-Square Approximation
• 11.4 Confidence Intervals
• 11.4 Confidence Intervals (cont.)
• 11.5 Results for Two-Parameter Models
• 11.6 Expected Information and Planning Experiments
• 11.7 Bias
• 17th February Study Week
24th February Revision Revision Revision
3rd March

MIDTERM

EXAMINATION

12 Tests of Significance
• 12.2 Likelihood Ratio Tests for Simple Hypotheses
• 12.3 Likelihood Ratio Tests for Composite Hypotheses
• 10th March
• 12.4 Tests for Binomial Probabilities
• 12.5 Tests for Multinomial Probabilities
• 12.5 Tests for Multinomial Probabilities (cont.)
• 12.6 Tests for Independence in Contingency Tables
• 12.7 Cause and Effect
• 12.8 Testing for Marginal Homogeneity
• 17th March
• 12.9 Significance Regions
• 15 Sufficient Statistics and Conditional Tests
• 15.1 The Sufficiency Principle
• 15.2 Properties of Sufficient Statistics
• 24th March
• 15.3 Exact Significance Levels and Coverage Probabilities
• 15.4 Choosing the Reference Set
• Snow Day
• 15.5 Conditional Tests for Composite Hypotheses
• 15.6 Some Examples of Conditional Tests
• 31st March
• 15.6 Some Examples of Conditional Tests (cont.)
• Revision Revision
7th April Revision END OF LECTURES