Journal: Scandinavian Journal of Statistics

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Abbreviation

Scand. j. statist.

Publisher

Wiley-Blackwell

Journal Volumes

ISSN

0303-6898
1467-9469

Description

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Publications 1 - 7 of 7
  • Buser, Christoph M.; Künsch, Hans R.; Weber, Alain (2010)
    Scandinavian Journal of Statistics
  • Most Likely Transformations
    Item type: Journal Article
    Hothorn, Torsten; Möst Lisa; Buehlmann, Peter (2018)
    Scandinavian Journal of Statistics
    We propose and study properties of maximum likelihood estimators in the class of conditional transformation models. Based on a suitable explicit parameterization of the uncon- ditional or conditional transformation function, we establish a cascade of increasingly complex transformation models that can be estimated, compared and analysed in the maximum likelihood framework. Models for the unconditional or conditional distribution function of any univariate response variable can be set up and estimated in the same theoretical and computational frame- work simply by choosing an appropriate transformation function and parameterization thereof. The ability to evaluate the distribution function directly allows us to estimate models based on the exact likelihood, especially in the presence of random censoring or truncation. For discrete and con- tinuous responses, we establish the asymptotic normality of the proposed estimators. A reference software implementation of maximum likelihood-based estimation for conditional transformation models that allows the same flexibility as the theory developed here was employed to illustrate the wide range of possible applications.
  • van de Geer, Sara (2014)
    Scandinavian Journal of Statistics
  • Muller, Patric; van de Geer, Sara (2015)
    Scandinavian Journal of Statistics
  • Meinshausen, Nicolai (2006)
    Scandinavian Journal of Statistics
  • Bhattacharya, Shrijita; Kamper, Francois; Beirlant, Jan (2023)
    Scandinavian Journal of Statistics
    Whether an extreme observation is an outlier or not depends strongly on the corresponding tail behavior of the underlying distribution. We develop an automatic, data-driven method rooted in the mathematical theory of extremes to identify observations that deviate from the intermediate and central characteristics. The proposed algorithm is an extension of a method previously proposed in the literature for the specific case of heavy tailed Pareto-type distributions to all max-domains of attraction. We propose some applications such as a tail-adjusted boxplot which yields a more accurate representation of possible outliers, and the identification of outliers in a multivariate context through an analysis of associated random variables such as local outlier factors. Several examples and simulation results illustrate the finite sample behavior of the algorithm and its applications.
  • Schelldorfer, Juerg; Bühlmann, Peter; van de Geer, Sara (2011)
    Scandinavian Journal of Statistics
Publications 1 - 7 of 7