PřF:MY101 Multivariate kernel estimation

MY101 Multivariate kernel estimation with general bandwidth matrices

Faculty of Science
Autumn 2010

Extent and Intensity: 0/5. 2 credit(s). Type of Completion: z (credit).
Teacher(s): prof. Jose E. Chacon (lecturer), prof. RNDr. Ivanka Horová, CSc. (deputy)
Guaranteed by: prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Course Enrolment Limitations: The course is offered to students of any study field.
Course objectives: Multivariate kernel density estimation is an important technique in statistical exploratory data analysis. Its utility relies on its ease of interpretation, especially by graphical means. The crucial factor which determines the performance of kernel density estimation is the bandwidth matrix selection. Research in finding optimal bandwidth matrices began with restricted parametrizations of the bandwidth matrix which mimic univariate selectors. Progressively these restrictions were relaxed to develop more flexible selectors. In these series of seminars we will explore some recent developments on multivariate kernel estimation with unconstrained bandwidth matrices. Some of the topics include general bandwidth selection for the estimation of the density and its derivatives, or construction of multivariate higher-order kernels.
Language of instruction: Czech
Further Comments: The course is taught only once.
The course is taught: in blocks.