Hiba Nassar

affiliated with the university, PhD, Master of Sceince

Research areas and keywords

UKÄ subject classification

  • Probability Theory and Statistics


  • Functional Data, Filtering , Functional Time Series


My current research project is to extend the functional Hodrick-Prescott filter to the case where the trend is an autoregressive order 1 or 2, which is the most prevalent model for functional time series data. The autoregressive model specifies that the output variable depends linearly on its own previous values. Existing approaches for estimating the FAR(p) model typically use functional principle components of covariance operators (Damon and Guillas, 2002, 2005; Horvath and Kokoszka, 2012; Kokoszka, 2012). However, in our study, we are aiming to get a better estimation of the autoregressive operator estimations. 


1. Teaching/Course responsibility:

- Role: lecturer

- Course name: Applied Multivariate Methods

- Course code: STAG18

- Number of clock hours: STAG18




(if there is more teaching, continue downwards)

2. Supervision of theses (including theses completed by Feb-19):

Undergraduate level (number of theses 2018): 1


Master level (number of theses 2018):0


3. Examiner of theses (including theses completed by Feb-19):

Undergraduate level (number of theses 2018):0


Master level (number of theses 2018):0



4. Briefly describe development work in education – renewal of curriculum and/or pedagogical development:



5. Involvement of guest teachers:


6. Programme responsibility (name of programme)



7. Additional work in education (e.g. planning, implementation, and pedagogical leadership)




8. Briefly describe your work with IT-supported teaching and learning



9. Production of cases, publications on teaching and learning, teaching materials and textbooks


Recent research outputs

Hiba Nassar & Djehiche, B., 2016 Mar, In : Journal of Inverse and Ill-Posed Problems (JIIP).

Research output: Contribution to journalArticle

Djehiche, B., Hilbert, A. & Hiba Nassar, 2016, In : Random Operators and Stochastic Equations. 24, 1, p. 33 42 p.

Research output: Contribution to journalArticle

View All (3)