250 episodes

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Mathematik, Informatik und Statistik - Open Access LMU - Teil 02/03 Ludwig-Maximilians-Universität München

    • Education

Die Universitätsbibliothek (UB) verfügt über ein umfangreiches Archiv an elektronischen Medien, das von Volltextsammlungen über Zeitungsarchive, Wörterbücher und Enzyklopädien bis hin zu ausführlichen Bibliographien und mehr als 1000 Datenbanken reicht. Auf iTunes U stellt die UB unter anderem eine Auswahl an elektronischen Publikationen der Wissenschaftlerinnen und Wissenschaftler an der LMU bereit. (Dies ist der 2. von 3 Teilen der Sammlung 'Mathematik, Informatik und Statistik - Open Access LMU'.)

    Generalized Additive Models with Unknown Link Function Including Variable Selection

    Generalized Additive Models with Unknown Link Function Including Variable Selection

    The generalized additive model is a well established and strong tool that allows to model smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, is misspecified, substantial bias is to be expected. A procedure is proposed that
    simultaneously estimates the form of the link function and the unknown form of the predictor functions including selection of predictors. The procedure is based on boosting methodology, which obtains estimates by using a sequence of weak learners. It strongly dominates fitting procedures that are unable to modify a given link function if the true link function deviates from the fixed function. The performance of the procedure is shown
    in simulation studies and illustrated by a real world example.

    On the Procrustean analogue of individual differences scaling (INDSCAL)

    On the Procrustean analogue of individual differences scaling (INDSCAL)

    In this paper, individual differences scaling (INDSCAL) is revisited, considering
    INDSCAL as being embedded within a hierarchy of individual difference scaling
    models. We explore the members of this family, distinguishing (i) models, (ii) the
    role of identification and substantive constraints, (iii) criteria for fitting models and (iv) algorithms to optimise the criteria. Model formulations may be based either on data that are in the form of proximities or on configurational matrices. In its configurational version, individual difference scaling may be formulated as a form of generalized Procrustes analysis. Algorithms are introduced for fitting the new
    models. An application from sensory evaluation illustrates the performance of the
    methods and their solutions.

    Global permutation tests for multivariate ordinal data: alternatives, test statistics, and the null dilemma

    Global permutation tests for multivariate ordinal data: alternatives, test statistics, and the null dilemma

    We discuss two-sample global permutation tests for sets of multivariate ordinal data in possibly high-dimensional setups, motivated by the analysis of data collected by means of the World Health Organisation's International Classification of Functioning,
    Disability and Health. The tests do not require any modelling of the multivariate dependence structure. Specifically, we consider testing for marginal inhomogeneity and
    direction-independent marginal order. Max-T test statistics are known to lead to good
    power against alternatives with few strong individual effects. We propose test statistics that can be seen as their counterparts for alternatives with many weak individual effects. Permutation tests are valid only if the two multivariate distributions are identical under the null hypothesis. By means of simulations, we examine the practical impact of violations of this exchangeability condition. Our simulations suggest that theoretically invalid permutation tests can still be 'practically valid'. In particular, they suggest that the degree of the permutation procedure's failure may be considered as a function of the difference in group-specific covariance matrices, the proportion between group sizes, the number of variables in the set, the test statistic used, and the number of levels per variable.

    Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm

    Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm

    In linear mixed models, the assumption of normally distributed random effects is often inappropriate and unnecessarily restrictive. The proposed approximate Dirichlet process mixture assumes a hierarchical Gaussian mixture that is based on the truncated version of the stick breaking presentation of the Dirichlet process. In addition to the weakening of distributional assumptions, the specification allows to identify clusters of observations with a similar random effects structure. An Expectation-Maximization algorithm is given that solves the estimation problem and that, in certain respects, may exhibit advantages over Markov chain Monte Carlo approaches when modelling with Dirichlet processes. The method is evaluated in a simulation study and applied to the dynamics of unemployment in Germany as well as lung function growth data.

    Variable selection with Random Forests for missing data

    Variable selection with Random Forests for missing data

    Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e. variable importance measures, can not be computed straightforward when there is missing data. Therefore an extensive simulation study has been conducted to explore possible solutions, i.e. multiple imputation, complete case analysis and a newly suggested importance measure for several missing data generating processes. The ability to distinguish relevant from non-relevant variables has been investigated for these procedures in combination with two popular variable selection methods. Findings and recommendations: Complete case analysis should not be applied as it lead to inaccurate variable selection and models with the worst prediction accuracy. Multiple imputation is a good means to select variables that would be of relevance in fully observed data. It produced the best prediction accuracy. By contrast, the application of the new importance measure causes a selection of variables that reflects the actual data situation, i.e. that takes the occurrence of missing values into account. It's error was only negligible worse compared to imputation.

    Modellierung der Heterogenität in Bradley-Terry-Luce Modellen

    Modellierung der Heterogenität in Bradley-Terry-Luce Modellen

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