SDST7609 Research methods in statistics (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof K Zhu, SCDS (Department of Statistics and Actuarial Science) < mazhuke@hku.hk >
Teachers Involved (Prof K Zhu,Statistics & Actuarial Science)
Course Objectives This course introduces some statistical concepts and methods which potential graduate students will find useful in preparing for work on a research degree in statistics.  Focus is on applications of state-of-the-art statistical techniques and their underlying theory.
Course Contents & Topics Contents may be selected from:
(1) Basic asymptotic methods: modes of convergence; stochastic orders; laws of large numbers; central limit theorems; delta method; Edgeworth expansions; saddlepoint approximations.
(2) Parametric and nonparametric likelihood methods: high-order approximations; profile likelihood and its variants; signed likelihood ratio statistics; empirical likelihood.
(3) Nonparametric statistical inference: sample quantiles; sign and rank tests; Kolmogorov-Smirnov test; nonparametric regression; density estimation; kernel methods.
(4) Computationally-intensive methods: cross-validation; bootstrap; permutation methods.
(5) Robust methods: measures of robustness; M-estimator; L-estimator; R-estimator; estimating functions.
(6) U-statistics, projection methods.
(7) Other topics as determined by the instructor.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 comprehend the language and technicalities found in statistical research literature
CLO 2 understand the use of standard mathematical tools for conducting statistical research
CLO 3 apply a variety of research tools to solve standard statistical problems
CLO 4 acquire exposure to some developments in contemporary statistical research
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3907
Only for students admitted in 2025 and thereafter.
Course to PLO Mapping
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 1,2,3,4
Examination One 2-hour written examination 60.0 1,2,3,4
Required/recommended reading
and online materials		 Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall: New York.
Owen, A.B. (2001). Empirical Likelihood. Chapman & Hall: Boca Raton.
Shao, J. (1999). Mathematical Statistics. Springer: New York.
Vaart, A. (1998). Asymptotic Statistics. Cambridge: Cambridge University Press.
Course Website http://moodle.hku.hk
Additional Course Information NIL