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Suche nach „[Hable] [Robert]“ hat 29 Publikationen gefunden
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    TC Grafenau

    Zeitschriftenartikel

    C. Dupke, C. Bonenfant, M. Ewald, M. Heurich, Robert Hable, B. Reineking, T. Zeppenfeld

    Habitat selection by a large herbivore at multiple spatial and temporal scales is primarily governed by food resources

    Ecography - Pattern and Process in Ecology, vol. 40, no. 8, pp. 1014-1027

    2017

    Abstract anzeigen

    Habitat selection can be considered as a hierarchical process in which animals satisfy their habitat requirements at different ecological scales. Theory predicts that spatial and temporal scales should co‐vary in most ecological processes and that the most limiting factors should drive habitat selection at coarse ecological scales, but be less influential at finer scales. Using detailed location data on roe deer Capreolus capreolus inhabiting the Bavarian Forest National Park, Germany, we investigated habitat selection at several spatial and temporal scales. We tested 1) whether time‐varying patterns were governed by factors reported as having the largest effects on fitness, 2) whether the trade‐off between forage and predation risks differed among spatial and temporal scales and 3) if spatial and temporal scales are positively associated. We analysed the variation in habitat selection within the landscape and within home ranges at monthly intervals, with respect to land‐cover type and proxys of food and cover over seasonal and diurnal temporal scales. The fine‐scale temporal variation follows a nycthemeral cycle linked to diurnal variation in human disturbance. The large‐scale variation matches seasonal plant phenology, suggesting food resources being a greater limiting factor than lynx predation risk. The trade‐off between selection for food and cover was similar on seasonal and diurnal scale. Habitat selection at the different scales may be the consequence of the temporal variation and predictability of the limiting factors as much as its association with fitness. The landscape of fear might have less importance at the studied scale of habitat selection than generally accepted because of the predator hunting strategy. Finally, seasonal variation in habitat selection was similar at the large and small spatial scales, which may arise because of the marked philopatry of roe deer. The difference is supposed to be greater for wider ranging herbivores.

    DigitalInstitut ProtectITTC Grafenau

    Zeitschriftenartikel

    Karl Leidl, Robert Hable, Michael Fernandes, Nari Arunraj, Michael Heigl

    Comparison of Supervised, Semi-supervised and Unsupervised Learning Methods in Network Intrusion Detection Systems (NIDS) Application

    Anwendungen und Konzepte in der Wirtschaftsinformatik (AKWI), no. 6, pp. 10-19

    2017

    Abstract anzeigen

    With the emergence of the fourth industrial revolution (Industrie 4.0) of cyber physical systems, intrusion detection systems are highly necessary to detect industrial network attacks. Recently, the increase in application of specialized machine learning techniques is gaining critical attention in the intrusion detection community. A wide variety of learning techniques proposed for different network intrusion detection system (NIDS) problems can be roughly classified into three broad categories: supervised, semi-supervised and unsupervised. In this paper, a comparative study of selected learning methods from each of these three kinds is carried out. In order to assess these learning methods, they are subjected to investigate network traffic datasets from an Airplane Cabin Demonstrator. In addition to this, the imbalanced classes (normal and anomaly classes) that are present in the captured network traffic data is one of the most crucial issues to be taken into consideration. From this investigation, it has been identified that supervised learning methods (logistic and lasso logistic regression methods) perform better than other methodswhen historical data on former attacks are available. The results of this study have also showed that the performance of semi-supervised learning method (One class support vector machine) is comparatively better than unsupervised learning method (Isolation Forest) when historical data on former attacks are not available.

    TC Grafenau

    Buch (Monographie)

    Robert Hable

    Einführung in die Stochastik

    Ein Begleitbuch zur Vorlesung

    SpringerSpektrum Lehrbuch, Berlin [u.a.]

    2015

    ISBN: 978-3-662-43497-0

    TC Grafenau

    Zeitschriftenartikel

    M. Wachten, Robert Hable, O. Fishkis

    Assessment of soil water repellency as a function of soil moisture with mixed modelling

    European Journal of Soil Science, vol. 66, no. 5, pp. 910-920

    2015

    DOI: 10.1111/ejss.12283

    Abstract anzeigen

    An understanding of the relation between soil water repellency (SWR) and soil moisture is a prerequisite of water-flow modelling in water-repellent soil. Here, the relation between SWR and soil moisture was investigated with intact cores of soil taken from three types of soil with different particle-size distributions. The SWR was measured by a sessile drop contact angle (CA) during drying at soil pF values that ranged from −∞ to 4.2. From the measured CA, the work of adhesion (Wa) was calculated and its relation with the pF-value was explored. Mixed modelling was applied to evaluate the effects of pF, soil type and soil depth on CA and Wa. For all soil types, a positive relation was observed between CA and the pF-value that could be represented by a linear model for the pF-range of 1–4.2. The variation in slope and intercept of the CA–pF relationship caused by heterogeneity of the samples taken from a single soil horizon was quantified. In addition, the relation between CA and water content (WC) showed hysteresis, with significantly larger CAs during drying than during wetting.

    DigitalTC Grafenau

    Zeitschriftenartikel

    S. Lauer, A.-L. Boulesteix, Robert Hable, M.J.A. Eugster

    A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies

    The American Statistician, vol. 69, no. 3, pp. 201-212

    2015

    DOI: 10.1080/00031305.2015.1005128

    Abstract anzeigen

    In computational sciences, including computational statistics, machine learning, and bioinformatics, it is often claimed in articles presenting new supervised learning methods that the new method performs better than existing methods on real data, for instance in terms of error rate. However, these claims are often not based on proper statistical tests and, even if such tests are performed, the tested hypothesis is not clearly defined and poor attention is devoted to the Type I and Type II errors. In the present article, we aim to fill this gap by providing a proper statistical framework for hypothesis tests that compare the performances of supervised learning methods based on several real datasets with unknown underlying distributions. After giving a statistical interpretation of ad hoc tests commonly performed by computational researchers, we devote special attention to power issues and outline a simple method of determining the number of datasets to be included in a comparison study to reach an adequate power. These methods are illustrated through three comparison studies from the literature and an exemplary benchmarking study using gene expression microarray data. All our results can be reproduced using R codes and datasets available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/compstud2013.

    TC Grafenau

    Zeitschriftenartikel

    A. Christmann, Robert Hable

    Estimation of Scale Functions to Model Heteroscedasticity by Kernel Based Quantile Methods

    Journal of Nonparametric Statistics, vol. 26, no. 2, pp. 219-239

    2014

    DOI: 10.1080/10485252.2013.875547

    Abstract anzeigen

    A main goal of regression is to derive statistical conclusions on the conditional distribution of the output variable Y given the input values x. Two of the most important characteristics of a single distribution are location and scale. Regularised kernel methods (RKMs) – also called support vector machines in a wide sense – are well established to estimate location functions like the conditional median or the conditional mean. We investigate the estimation of scale functions by RKMs when the conditional median is unknown, too. Estimation of scale functions is important, e.g. to estimate the volatility in finance. We consider the median absolute deviation (MAD) and the interquantile range as measures of scale. Our main result shows the consistency of MAD-type RKMs.

    TC Grafenau

    Zeitschriftenartikel

    M. Fezakov, C. Samimi, Robert Hable, A. Abdulnazarov, B. Mislimshoeva, T. Koellner

    Factors Influencing Households' Firewood Consumption in the Western Pamirs, Tajikistan

    Mountain Research and Development, vol. 34, no. 2, pp. 147-156

    2014

    DOI: 10.1659/MRD-JOURNAL-D-13-00113.1

    Abstract anzeigen

    Firewood is a major energy source, especially in many high mountainous regions in developing countries where other energy sources are limited. In the mountainous regions of Tajikistan, current energy consumption is limited owing to geographic isolation and numerous challenges—including in the energy sector—that emerged after the collapse of the Soviet Union and Tajikistan's independence. The sudden disruption of external supplies of energy forced people to rely on locally available but scarce biomass resources, such as firewood and animal dung. We conducted an empirical study to gain an understanding of current household energy consumption in the Western Pamirs of Tajikistan and the factors that influence firewood consumption. For this purpose, we interviewed members of 170 households in 8 villages. We found that, on average, households consumed 355 kg of firewood, 253 kWh of electricity, 760 kg of dung, and 6 kg of coal per month in the winter of 2011–2012. Elevation, size of a household's private garden, and total hours of heating had a positive relationship with firewood consumption, and education level and access to a reliable supply of electricity showed a negative relationship.

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    A. Christmann, Robert Hable

    On the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods

    Proceedings of the 59th World Statistics Congress of the International Statistical Institute (ISI) [August 25th - 30th 2013, Hong Kong]

    2013

    TC Grafenau

    Zeitschriftenartikel

    Robert Hable, D. Skulj

    Coefficients of ergodicity for Markov chains with uncertain parameters

    Metrika, vol. 76, no. 1, pp. 107-133

    2013

    Abstract anzeigen

    ne of the central considerations in the theory of Markov chains is their convergence to an equilibrium. Coefficients of ergodicity provide an efficient method for such an analysis. Besides giving sufficient and sometimes necessary conditions for convergence, they additionally measure its rate. In this paper we explore coefficients of ergodicity for the case of imprecise Markov chains. The latter provide a convenient way of modelling dynamical systems where parameters are not determined precisely. In such cases a tool for measuring the rate of convergence is even more important than in the case of precisely determined Markov chains, since most of the existing methods of estimating the limit distributions are iterative. We define a new coefficient of ergodicity that provides necessary and sufficient conditions for convergence of the most commonly used class of imprecise Markov chains. This so-called weak coefficient of ergodicity is defined through an endowment of the structure of a metric space to the class of imprecise probabilities. Therefore we first make a detailed analysis of the metric properties of imprecise probabilities.

    TC Grafenau

    Zeitschriftenartikel

    Robert Hable

    Universal Consistency of Localized Versions of Regularized Kernel Methods

    Journal of Machine Learning Research, vol. 14, no. 1, pp. 111-144

    2013

    Abstract anzeigen

    In supervised learning problems, global and local learning algorithms are used. In contrast to global learning algorithms, the prediction of a local learning algorithm in a testing point is only based on training data which are close to the testing point. Every global algorithm such as support vector machines (SVM) can be localized in the following way: in every testing point, the (global) learning algorithm is not applied to the whole training data but only to the k nearest neighbors (kNN) of the testing point. In case of support vector machines, the success of such mixtures of SVM and kNN (called SVM-KNN) has been shown in extensive simulation studies and also for real data sets but only little has been known on theoretical properties so far. In the present article, it is shown how a large class of regularized kernel methods (including SVM) can be localized in order to get a universally consistent learning algorithm.

    TC Grafenau

    Zeitschriftenartikel

    A. Christmann, Robert Hable

    Consistency of support vector machines using additive kernels for additive models

    Computational Statistics & Data Analysis, vol. 56, no. 4, pp. 854-873

    2012

    DOI: 10.1016/j.csda.2011.04.006

    Abstract anzeigen

    Support vector machines (SVMs) are special kernel based methods and have been among the most successful learning methods for more than a decade. SVMs can informally be described as kinds of regularized MM-estimators for functions and have demonstrated their usefulness in many complicated real-life problems. During the last few years a great part of the statistical research on SVMs has concentrated on the question of how to design SVMs such that they are universally consistent and statistically robust for nonparametric classification or nonparametric regression purposes. In many applications, some qualitative prior knowledge of the distribution View the MathML sourceP or of the unknown function ff to be estimated is present or a prediction function with good interpretability is desired, such that a semiparametric model or an additive model is of interest. The question of how to design SVMs by choosing the reproducing kernel Hilbert space (RKHS) or its corresponding kernel to obtain consistent and statistically robust estimators in additive models is addressed. An explicit construction of such RKHSs and their kernels, which will be called additive kernels, is given. SVMs based on additive kernels will be called additive support vector machines . The use of such additive kernels leads, in combination with a Lipschitz continuous loss function, to SVMs with the desired properties for additive models. Examples include quantile regression based on the pinball loss function, regression based on the ϵϵ-insensitive loss function, and classification based on the hinge loss function.

    TC Grafenau

    Zeitschriftenartikel

    Robert Hable

    Asymptotic Normality of Support Vector Machine Variants and Other Regularized Kernel Methods

    Journal of Multivariate Analysis, vol. 106, no. April, pp. 92-117

    2012

    DOI: 10.1016/j.jmva.2011.11.004

    Abstract anzeigen

    In nonparametric classification and regression problems, regularized kernel methods, in particular support vector machines, attract much attention in theoretical and in applied statistics. In an abstract sense, regularized kernel methods (simply called SVMs here) can be seen as regularized M-estimators for a parameter in a (typically infinite dimensional) reproducing kernel Hilbert space. For smooth loss functions LL, it is shown that the difference between the estimator, i.e. the empirical SVM View the MathML sourcefL,Dn,λDn, and the theoretical SVM fL,P,λ0fL,P,λ0 is asymptotically normal with rate View the MathML sourcen. That is, View the MathML sourcen(fL,Dn,λDn−fL,P,λ0) converges weakly to a Gaussian process in the reproducing kernel Hilbert space. As common in real applications, the choice of the regularization parameter View the MathML sourceDn in View the MathML sourcefL,Dn,λDn may depend on the data. The proof is done by an application of the functional delta-method and by showing that the SVM-functional P↦fL,P,λP↦fL,P,λ is suitably Hadamard-differentiable.

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    M. Troffaes, Robert Hable

    Robustness of Natural Extension

    Proceedings of the 7th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'11) [July 25th - 28th 2011, Innsbruck, Austria]

    2011

    TC Grafenau

    Zeitschriftenartikel

    A. Christmann, Robert Hable

    On Qualitative Robustness of Support Vector Machines

    Journal of Multivariate Analysis, vol. 102, no. 6, pp. 993-1007

    2011

    DOI: 10.1016/j.jmva.2011.01.009

    Abstract anzeigen

    Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistics. The main topics of recent interest are consistency, learning rates and robustness. We address the open problem whether SVMs are qualitatively robust. Our results show that SVMs are qualitatively robust for any fixed regularization parameter λλ. However, under extremely mild conditions on the SVM, it turns out that SVMs are not qualitatively robust any more for any null sequence λnλn, which are the classical sequences needed to obtain universal consistency. This lack of qualitative robustness is of a rather theoretical nature because we show that, in any case, SVMs fulfill a finite sample qualitative robustness property. For a fixed regularization parameter, SVMs can be represented by a functional on the set of all probability measures. Qualitative robustness is proven by showing that this functional is continuous with respect to the topology generated by weak convergence of probability measures. Combined with the existence and uniqueness of SVMs, our results show that SVMs are the solutions of a well-posed mathematical problem in Hadamard’s sense.

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    T. Kroupa, Robert Hable

    Structure of the Set of Belief Functions Generated by a Random Closed Interval

    Proceedings of the Workshop on the Theory of Belief Functions (European Mathematical Society, March 30th - April 1st 2010, Brest, France)

    2010

    TC Grafenau

    Zeitschriftenartikel

    H. Rieder, P. Ruckdeschel, Robert Hable

    Optimal robust influence functions in semiparametric regression

    Journal of Statistical Planning and Inference, vol. 140, no. 1, pp. 226-245

    2010

    DOI: 10.1016/j.jspi.2009.07.010

    Abstract anzeigen

    Robust statistics allows the distribution of the observations to be any member of a suitable neighborhood about an ideal model distribution. In this paper, the ideal models are semiparametric with finite-dimensional parameter of interest and a possibly infinite-dimensional nuisance parameter. In the asymptotic setup of shrinking neighborhoods, we derive and study the Hampel-type problem and the minmax MSE-problem. We show that, for all common types of neighborhood systems, the optimal influence function View the MathML sourceψ˜ can be approximated by the optimal influence functions View the MathML sourceψ˜n for certain parametric models. For general semiparametric regression models, we determine View the MathML source(ψ˜n)n∈N in case of error-in-variables and in case of error-free-variables. Finally, the results are applied to Cox regression where we compare our approach to that of Bednarski [1993. Robust estimation in Cox's regression model. Scand. J. Statist. 20, 213–225] in a small simulation study and on a real data set.

    TC Grafenau

    Zeitschriftenartikel

    Robert Hable

    Minimum Distance Estimation in Imprecise Probability Models

    Journal of Statistical Planning and Inference, vol. 140, no. 2, pp. 461-479

    2010

    Abstract anzeigen

    The present article considers estimating a parameter θθ in an imprecise probability model View the MathML source(P¯θ)θ∈Θ. This model consists of coherent upper previsions View the MathML sourceP¯θ which are given by finite numbers of constraints on expectations. A minimum distance estimator is defined in this case and its asymptotic properties are investigated. It is shown that the minimum distance can be approximately calculated by discretizing the sample space. Finally, the estimator is applied in a simulation study and on a real data set.

    TC Grafenau

    Zeitschriftenartikel

    T. Augustin, Robert Hable

    On the impact of robust statistics on imprecise probability models: a review

    Structural Safety, vol. 32, no. 6, pp. 358-365

    2010

    DOI: 10.1016/j.strusafe.2010.06.002

    Abstract anzeigen

    Robust statistics is concerned with statistical methods that still lead to reliable conclusions if an ideal model is only approximately true. More recently, the theory of imprecise probabilities was developed as a general methodology to model non-stochastic uncertainty (ambiguity) adequately, and has been successfully applied to many engineering problems. In robust statistics, small deviations from ideal models are modeled by certain neighborhoods. Since nearly all commonly used neighborhoods are imprecise probabilities, a large part of robust statistics can be seen as a special case of imprecise probabilities. Therefore, it seems quite promising to address problems in the theory of imprecise probabilities by trying to generalize results of robust statistics. In this review paper, we present some cases where this has already been done successfully and where the connections between (frequentist) robust statistics and imprecise probabilities are most striking.

    TC Grafenau

    Zeitschriftenartikel

    Robert Hable

    A Minimum Distance Estimator in an Imprecise Probability Model - Computational Aspects and Applications

    International Journal of Approximate Reasoning, vol. 51, no. 9, pp. 1114-1128

    2010

    DOI: 10.1016/j.ijar.2010.08.003

    Abstract anzeigen

    The article considers estimating a parameter θ in an imprecise probability model View the MathML source(P¯θ)θ∈Θ which consists of coherent upper previsions View the MathML sourceP¯θ. After the definition of a minimum distance estimator in this setup and a summarization of its main properties, the focus lies on applications. It is shown that approximate minimum distances on the discretized sample space can be calculated by linear programming. After a discussion of some computational aspects, the estimator is applied in a simulation study consisting of two different models. Finally, the estimator is applied on a real data set in a linear regression model.

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Robert Hable, D. Skulj

    Coefficients of ergodicity for imprecise Markov chaines

    Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'09) [July 14th - 18th 2009, Department of Mathematical Sciences, Durham University, Durham, UK]

    2009

    Abstract anzeigen

    Coefficients of ergodicity are an important tool in measuring convergence of Markov chains. We explore possibilities to generalise the concept to imprecise Markov chains. We find that this can be done in at least two different ways, which both have interesting implications in the study of convergence of imprecise Markov chains. Thus we extend the existing definition of the uniform coefficient of ergodicity and define a new so-called weak coefficient of ergodicity. The definition is based on the endowment of a structure of a metric space to the class of imprecise probabilities. We show that this is possible to do in some different ways, which turn out to coincide.