SDST3602 Statistical inference (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof S M S Lee, SCDS (Department of Statistics and Actuarial Science) < smslee@hku.hk >
Teachers Involved (Prof S M S Lee,Statistics & Actuarial Science)
Course Objectives This course covers the advanced theory of point estimation, interval estimation and hypothesis testing.  Using a mathematically-oriented approach, the course provides a solid and rigorous treatment of inferential problems, statistical methodologies and the underlying concepts and theory.  It is suitable in particular for students intending to further their studies or to develop a career in statistical research.
Course Contents & Topics 1. Decision problem - frequentist approach: loss function; risk; decision rule; admissibility; minimaxity; unbiasedness; Bayes' rule.
2. Decision problem - Bayesian approach: prior and posterior distributions, Bayesian inference.
3. Estimation theory: exponential families; likelihood; sufficiency; minimal sufficiency; completeness; UMVU estimators; information inequality; large-sample theory of maximum likelihood estimation.
4. Hypothesis testing: uniformly most powerful test; monotone likelihood ratio; UMP unbiased test; conditional test;  large-sample theory of likelihood ratio; confidence set.
5. Nonparametric inference: bootstrap methods.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 form a panoramic view of classical developments in mathematical statistics
CLO 2 gain thorough insight into the essentials of statistical inference
CLO 3 build a solid foundation for future research studies in statistics and related areas
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST2602 or SDST3902
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Major in Statistics < PLO 1,2,4,5,6 >
2023 Major in Statistics < PLO 1,2,4,5,6 >
2022 Major in Statistics < PLO 1,2,4,5,6 >
2021 Major in Statistics < PLO 1,2,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    Examination Dec     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 40.0 1,2,3
Examination One 2-hour written examination 60.0 1,2,3
Required/recommended reading
and online materials		 Berry, D. A. & Lindgren, B. W.: Statistics: Theory and Methods (Duxbury, Belmont, 1996).
Bickel, P. J. & Doksum, K. A.: Mathematical Statistics: Basic Ideas and Selected Topics, Vol. 1 (Prentice Hall, Upper Saddle River, N.J., 2001).
Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall: New York.
Freund, J. E.: Mathematical Statistics (Prentice Hall, Englewood Cliffs, N.J., 1992).
Hogg, R. V. & Craig, A. T.: Introduction to Mathematical Statistics (Macmillan, New York, 1989).
Pace, L. & Salvan, A.: Principles of Statistical Inference: from a neo-Fisherian perspective (World Scientific: Singapore, 1997).
Wasserman, L. (2006). All of Nonparametric Statistics. Springer.
Young, G.A. & Smith, R.L.: Essentials of Statistical Inference (Cambridge University Press: Cambridge, 2005).
Course Website http://moodle.hku.hk
Additional Course Information NIL