Strategies for penalised least squares estimators of functional regression models

  • Thu 23 Jan 20

    14:00 - 16:00

  • Colchester Campus

    STEM 3.1

  • Event speaker

    Dr Stella Hadjiantoni

  • Event type

    Lectures, talks and seminars
    Mathematical Sciences Departmental Seminar

  • Event organiser

    Mathematical Sciences, Department of

  • Contact details

    Andrew Harrison

Mathematical Sciences Departmental Seminar

These Departmental Seminars are for everyone interested in Maths. We encourage anyone interested in the subject in general, or in the particular subject of the seminar, to come along. It's a great opportunity to meet people in the Maths Department and join in with our community. 

Refreshments are shared in the Department (STEM 5.1) after every seminar.

Strategies for penalised least squares estimators of functional regression models

In functional data analysis, the discrete observed data are converted to smooth functions and so they become infinite dimensional data objects.

The analysis involves representing the functional data using a basis expansion and then truncating the basis in term of a finite number of basis elements. Choosing the number of basis elements is part of the data analysis.

Therefore, the dimension of the basis expansion is an unknown parameter and investigation is required to determine its value. A recursive numerical method is examined for choosing the number of basis elements within the context of model selection. Penalised least squares and cross validation procedures are used in order to choose the number of basis elements that optimise the estimation of the functional regression model. The proposed numerical method is based on orthogonal and hyperbolic transformations.


Dr Stella Hadjiantoni is a Lecturer in Data Science and Statistics in the Department of Mathematical Sciences.

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