Computational methods in statistical analysis

Additional Info

  • ECTS credits: 6
  • University: University of Nice - Sophia Antipolis
  • Semester: 3
  • Objectives:

     

    This course will focus on several standard computational methods in data sciences and on model selection in statistics. This includes: Concept of statistical model, Supervised model selection, Cross-Validation techniques (Hold-out, V-Fold, Leave-one-out, Leave q-out), Unsupervised model selection, Penalized criteria (AIC, BIC, ICL, slope heuristic), applications of model selection to regression (ridge, lasso), density estimation, model-based classification and clustering, variable selection, dimension reduction (probabilistic PCA).

  • Topics:

     

    Supervised and unsupervised model selection, Cross-validation techniques, Penalized criteria, Applications to regression.

Read 6425 times Last modified on Tuesday, 20 February 2018 17:15
Home About Course units Computational methods in statistical analysis

Connect with us

Our partners' addresses

University of L'Aquila, Italy (UAQ)

Department of Information Engineering, Computer Science and Mathematics, via Vetoio (Coppito), 1 – 67100 L’Aquila (Italy)

University of Hamburg , Germany (UHH)

Department of Mathematics
Bundesstr. 55
20146 Hamburg - Germany

University of Côte d'Azur, Nice - France (UCA)

Laboratoire J.A.Dieudonné
Parc Valrose, France-06108 NICE Cedex 2