MATH5895 is an honours and postgraduate mathematics course. See the course overview below.
Units of credit: 6
Prerequisites: There are no prerequisites for this course. However, students are assumed to be acquainted with the basic principles of Probability and Statistics theory (random variables and their characteristics, estimators and their properties (bias, variance, consistency, asymptotic distribution), law of large numbers and central limit theorem, maximum likelihood methods. Moreover, they are expected to have basic knowledge of Real Analysis (functions and their properties, limits and series, differentials and integrals, Taylor expansions and function spaces).
Cycle of offering: Term 3
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines
The course handout contains information about course objectives, assessment, course materials and the syllabus and will be made available closer to Term of offering.
Important additional information as of 2023
The University requires all students to be aware of its policy on plagiarism.
For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.
If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion..
The online handbook entry contains information about the course. The timetable is only up-to-date if the course is being offered this year.
If you are currently enrolled in MATH5895, you can log into UNSW Moodle for this course.
This course aims at introducing the "modern" nonparametric techniques in statistical analysis and the use of these techniques in a variety of disciplines. "Modern" nonparametric statistics essentially refer to the so-called smoothing procedures for curve estimation, in contrast to traditional nonparametric methods such as rank-based tests.
The main idea of this course is to get students acquainted with the fundamentals, basic properties and use of the most important recent nonparametric techniques. Another aim is to familiarise students with research questions in this domain.