Skip to main content

7 Result(s)

within P. Filzmoser Remove this filter Statistics Remove this filter

and

  1. Article

    Open Access

    Robust logistic zero-sum regression for microbiome compositional data

    We introduce the Robust Logistic Zero-Sum Regression (RobLZS) estimator, which can be used for a two-class problem with high-dimensional compositional covariates. Since the log-contrast model is employed, the ...

    G. S. Monti, P. Filzmoser in Advances in Data Analysis and Classification (2022)

    Download PDF (800 KB) View Article

  2. No Access

    Article

    Robust second-order least-squares estimation for regression models with autoregressive errors

    Rosadi and Peiris (Comput Stat 29:931–943, 2014) applied the second-order least squares estimator (SLS), which was proposed in Wang and Leblanc (Ann Inst of Stat Math 60:883–900, 2008), to regression models with ...

    D. Rosadi, P. Filzmoser in Statistical Papers (2019)

  3. Article

    Erratum to: Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator

    N. M. Neykov, P. Filzmoser, P. N. Neytchev in Statistical Papers (2014)

    Download PDF (86 KB) View Article

  4. No Access

    Article

    Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator

    The penalized maximum likelihood estimator (PMLE) has been widely used for variable selection in high-dimensional data. Various penalty functions have been employed for this purpose, e.g., Lasso, weighted Las...

    N. M. Neykov, P. Filzmoser, P. N. Neytchev in Statistical Papers (2014)

  5. No Access

    Article

    Robust canonical correlations: A comparative study

    Several approaches for robust canonical correlation analysis will be presented and discussed. A first method is based on the definition of canonical correlation analysis as looking for linear combinations of t...

    J. A. Branco, C. Croux, P. Filzmoser, M. R. Oliveira in Computational Statistics (2005)

  6. No Access

    Article

    Testing hypotheses with fuzzy data: The fuzzy p-value

    Statistical hypothesis testing is very important for finding decisions in practical problems. Usually, the underlying data are assumed to be precise numbers, but it is much more realistic in general to conside...

    P. Filzmoser, R. Viertl in Metrika (2004)

  7. No Access

    Chapter and Conference Paper

    Robust Redundancy Analysis by Alternating Regression

    Given two groups of variables redundancy analysis searches for linear combinations of variables in one group that maximize the variance of the other group that is explained by each one of the linear combinatio...

    M. R. Oliveira, J. A. Branco, C. Croux… in Theory and Applications of Recent Robust M… (2004)