• Local Likelihood 

      Otneim, Håkon (Master thesis, 2012-04-23)
      Methods for probability density estimation are traditionally classified as either parametric or non-parametric. Fitting a parametric model to observations is generally a good idea when we have sufficient information on the ...
    • Multivariate and conditional density estimation using local Gaussian approximations 

      Otneim, Håkon (Doctoral thesis, 2016-09-23)
      Paper 1 ”Bias and bandwidth for local likelihood density estimation”: A local likelihood density estimator is shown to have asymptotic bias depending on the dimension of the local parameterization. Comparing with kernel ...
    • Pairwise local Fisher and naive Bayes: Improving two standard discriminants 

      Otneim, Håkon; Jullum, Martin; Tjøstheim, Dag Bjarne (Journal article; Peer reviewed, 2020)
      The Fisher discriminant is probably the best known likelihood discriminant for continuous data. Another benchmark discriminant is the naive Bayes, which is based on marginals only. In this paper we extend both discriminants ...
    • Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations 

      Sleire, Anders Daasvand; Støve, Bård; Otneim, Håkon; Berentsen, Geir Drage; Tjøstheim, Dag Bjarne; Haugen, Sverre Hauso (Journal article; Peer reviewed, 2021)
      It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework that incorporates such asymmetric ...