@inproceedings{AntretterMayerWellisch2011, author = {Antretter, F. and Mayer, C. and Wellisch, Ulrich}, title = {An approach for a statistical model for the user behaviour regarding window ventilation in residential buildings}, series = {Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney}, booktitle = {Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney}, year = {2011}, language = {en} } @techreport{AuerBenekenBrummeretal.2017, author = {Auer, Veronika and Beneken, Gerd and Brummer, Benjamin and Ch{\^a}teauvieux-Hellwig, Camille and Engler, Benjamin and Gilly, Alexander and Hagl, Rainer and Hummel, Felix and Hummel, Sabine and Karlinger, Peter and Knorr, Ludwig and K{\"o}ster, Heinrich and Kucich, Martin and Mecking, Simon and Rabold, Andreas and Sandor, Viktor and Schalk, Daniel and Schanda, Ulrich and Schemme, Michael and Schiffner, Ivonne and Schmidt, Jochen and Schugmann, Reinhard and Seidlmeier, Heinrich and Sigg, Ferdinand and Stauss, Kilian and Sussmann, Monika and Wellisch, Ulrich and Wenninger, Marc and Wittmann, Josef and Zscheile, Matthias}, title = {Jahresbericht 2016, Forschung - Entwicklung - Innovation}, organization = {Hochschule Rosenheim}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-12282}, pages = {68}, year = {2017}, abstract = {Mit dem j{\"a}hrlich erscheinenden Forschungsbericht m{\"o}chte die Hochschule Rosenheim einen Einblick in ihre vielf{\"a}ltigen Projekte und Aktivit{\"a}ten der angewandten Forschung und Entwicklung geben. Im Jahresbericht 2016 wird {\"u}ber Vorhaben im Jahr 2016 berichtet.}, language = {de} } @techreport{BarthBeneckenBetzetal.2016, author = {Barth, Simon and Benecken, Gerd and Betz, Andreas and Binninger, Karsten and Graule, Verena and Hack, Andreas and Haegele, Rainer and Hartmann, Markus and Hauck-Bauer, Eva and Haut, Sandra and J{\"a}ger, Johannes and Kagerl, Andreas and Karlinger, Peter and Konle, Elke and K{\"o}ster, Heinrich and Krause, Harald and Krommes, Sandra and Meissner, Thomas and Michanickl, Andreas and Sandor, Viktor and Schanda, Ulrich and Schemme, Michael and Sebald, Daniela and Spindler, Uli and Stauss, Kilian and Strobl, Thomas and Sussmann, Monika and Versen, Martin and Voit, Johann and Wambsganß, Mathias and Wellisch, Ulrich and Zauner, Johannes}, title = {Forschungsbericht 2015}, organization = {Hochschule Rosenheim}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-12304}, pages = {76}, year = {2016}, abstract = {Mit dem j{\"a}hrlich erscheinenden Forschungsbericht m{\"o}chte die Hochschule Rosenheim einen Einblick in ihre vielf{\"a}ltigen Projekte und Aktivit{\"a}ten der angewandten Forschung und Entwicklung geben. Im Forschungsbericht 2015 wird {\"u}ber Vorhaben im Jahr 2015 berichtet.}, language = {de} } @techreport{BauerFeldmeierPaulusetal.2014, author = {Bauer, L. and Feldmeier, F. and Paulus, F. and Schreyer, M. and Wellisch, Ulrich}, title = {In situ Pr{\"u}fverfahren f{\"u}r transparente Bauteile}, year = {2014}, language = {de} } @techreport{BauerBeneckenBueckeretal.2015, author = {Bauer, Lucia and Benecken, Gerd and B{\"u}cker, Dominikus and Buff, Alexander and Carlton, Katrina and Feldmeier, Franz and Flatscher, Simon and Hack, Andreas and Halt, Manfred and Jell, Peter and K{\"o}ster, Heinrich and Kucich, Martin and Manzinger, Franziska and Mecking, Simon and Paus, Inger and Rex, Steffen and Schanda, Ulrich and Schreyer, Manuela and Spindler, Uli and Stadler, Constanze and Stahnke, Svenja and Stiegler, Gertrud and Walser, Georg and Wambsganß, Mathias and Wellisch, Ulrich}, title = {Forschungsbericht 2014}, organization = {Hochschule Rosenheim}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-12317}, pages = {68}, year = {2015}, abstract = {Mit dem j{\"a}hrlich erscheinenden Forschungsbericht m{\"o}chte die Hochschule Rosenheim einen Einblick in ihre vielf{\"a}ltigen Projekte und Aktivit{\"a}ten der angewandten Forschung und Entwicklung geben. Im Forschungsbericht 2014 wird {\"u}ber Vorhaben im Jahr 2014 berichtet.}, language = {de} } @book{BeckerHerrmannSandoretal.2016, author = {Becker, T. and Herrmann, R. and Sandor, V. and Wellisch, Ulrich}, title = {Stochastische Risikomodellierung und statistische Methoden}, publisher = {Springer}, address = {Berlin}, publisher = {Technische Hochschule Rosenheim}, year = {2016}, abstract = {Aktuare treffen, genauso wie Finanzmathematiker und Risikomanager, jeden Tag Einsch{\"a}tzungen {\"u}ber Risiken. Unter Risiken verstehen sie dabei Objekte, an denen sich zuf{\"a}llige Ph{\"a}nomene realisieren, und die sich somit einer sicheren Einsch{\"a}tzung entziehen. Dabei bedienen sich die Aktuare stochastischer Risikomodelle, um � Erkl{\"a}rungsmuster f{\"u}r historische Beobachtungen der Risiken zu finden, � Zukunftsvorhersagenbez{\"u}glich der Risiken zu treffen und � die Genauigkeit der getroffenen Einsch{\"a}tzungen und Prognosen zu bewerten. Stochastische Risikomodelle sind mithin ein Instrument, um die Einsch{\"a}tzung von Risiken zu objektivieren. Risikomodelle werden in der Regel mit statistischen Methoden an Beobachtungsdaten angepasst. Aus einem Modell heraus getroffene Aussagen werden sp{\"a}testens hierdurch zu Einsch{\"a}tzungen, die - um mit Descartes zu sprechen - lediglich „wahrscheinlich" korrektsind. Hier kannein stochastisches Modell helfen, die Wahrscheinlichkeit zu quantifizieren, mit der eine gegebene Einsch{\"a}tzung falsch ist, und die Wahl einer geeigneten statistischen Methode zur Modellanpassung kann helfen, diese Wahrscheinlichkeit zu reduzieren. Stochastische Risikomodelle und statistische Methoden erlauben somit einen wissenschaftlich korrekten Umgang mit dem von Descartes aufgezeigten Dilemma. Allerdings: Risikomodelle sind k{\"u}nstlich - sie leben von den Annahmen, die ihnen zugrunde liegen. F{\"u}r eine seri{\"o}se Modellbildung ist daher das Verst{\"a}ndnis und die Validierung der Modellannahmen unverzichtbar, wie auch ein {\"U}berblick {\"u}ber m{\"o}gliche alternative Modelle. Das vorliegendeBuchm{\"o}chte daherdie wichtigsten stochastischen Risikomodelle, die derzeit in der aktuariellen Praxis Anwendung finden,in einem Band zusammenfassen. Dabei wird Wert auf eine mathematisch fundierte,aber dennoch f{\"u}r Theoretiker und Praktiker gut verst{\"a}ndliche und interessante Darstellung der Modelle mit ihren Eigenschaften und Annahmen gelegt. Gleichzeitig werden die statistischen Methoden zur Modellanpassung bereitgestellt. Die behandelten Modelle und Methoden sind auch die Grundlage, auf denen zahlreiche Anwendungen in Finanzmathematik und quantitativem Risikomanagement aufbauen.}, language = {de} } @article{FreySchoenfelderWellisch2016, author = {Frey, C. and Sch{\"o}nfelder, F. and Wellisch, Ulrich}, title = {Anwendung von maschinellem Lernen in der Tarifierung}, series = {Der Aktuar}, journal = {Der Aktuar}, year = {2016}, language = {de} } @inproceedings{HanfstaenglParzingerSpindleretal.2020, author = {Hanfstaengl, Lucia and Parzinger, Michael and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions}, series = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, volume = {172}, booktitle = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, pages = {22005}, year = {2020}, abstract = {Knowing about the presence and number of people in a room can be of interest for precise control of heating, ventilation and air conditioning. To determine the number and presence of occupants cost-effectively, it is of interest to use already existing air condition sensors (temperature, humidity, CO2) of the building automation system. Different approaches and methods for determining presence have attracted attention in recent years. We propose an occupancy detection method based on a method of supervised machine learning. In an experiment, measurement data were recorded in a research apartment with controllable boundary conditions. The presence of people was simulated by artificial injection of water vapour, CO2 and heat dissipation. The variation of the number of artificial users, the duration of presence and the supply air volume flow of the ventilation resulted in a total of 720 combinations. By using artificial users, the boundary conditions were accurately defined, and different presence situations could be measured time-effectively. The data is evaluated with a method of supervised machine learning called random forest. The statistical model can determine precisely the number of people in over 93\% of the cases in a disjoint test sample. The experiments took part in the Rosenheim Technical University of Applied Sciences laboratory.}, language = {en} } @inproceedings{HanfstaenglParzingerWirnsbergeretal.2019, author = {Hanfstaengl, Lucia and Parzinger, Michael and Wirnsberger, Markus and Spindler, Uli and Wellisch, Ulrich}, title = {Identifying The Presence Of People In A Room Based On Machine Learning Techniques Using Data Of Room Control Systems}, series = {IEA EBC Annex 71: Building energy performance assessment based on in-situ measurements, 6th expert meeting, April 08.-10. 2019 - Bilbao, ES}, booktitle = {IEA EBC Annex 71: Building energy performance assessment based on in-situ measurements, 6th expert meeting, April 08.-10. 2019 - Bilbao, ES}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-14077}, pages = {1 -- 7}, year = {2019}, abstract = {When monitoring the energy performance of buildings, it may be of interest to identify the occupation periods of people in the room due to their possible impact on the energy balance. In order to be able to carry out a comprehensive energy assessment of the system and building, it is necessary to be able to classify user influence during the evaluation. This thesis investigates how the presence of people in a room can be determined cost-effectively and with little additional effort. The aim is to determine which sensors of a room control system provide sufficiently reliable data. The presence of 1-2 persons was examined on a test facility of the Technical University Rosenheim. The air-, mean radiation- and surface-temperatures, the air humidity as well as the CO2 and VOC concentrations were measured. For the analysis, a method of supervised machine and statistical learning, random forest, is used. The smallest model error detected in predicting the presence of 1 or 2 persons from CO2 sensor data is 1.43\%. The error rates are low for all tested models if time-dynamic effects are used as predictors and the data is processed in a so-called time period form. Additionally, the ways in which this data should ideally be made available for future measurements and processed to facilitate analysis with machine and statistical learning techniques have been investigated. A further goal is to apply the models developed on measurement series in laboratory environments to real rooms and to assess the transferability of these models.}, language = {en} } @techreport{KuechenhoffWellisch1997, author = {K{\"u}chenhoff, H. and Wellisch, Ulrich}, title = {Asymptotics for generalized linear segmented regression models with an unknown breakpoint}, year = {1997}, abstract = {We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model with an unknown breakpoint. A proof for the asymptotic normality is given. The methods are based on the work of Huber (1967). The main problem is the non--differentiability of the likelihood and the score function, which requires non--standard methods. An example from epidemiology is presented, where confidence intervals for the parameters are calculated with the asymptotic results.}, language = {en} } @inproceedings{MeckingScheibengraberKruseetal.2017, author = {Mecking, Simon and Scheibengraber, Markus and Kruse, Tobias and Schanda, Ulrich and Wellisch, Ulrich}, title = {Energiebestimmung an Brettsperrholzbauteilen im Holzmassivbau}, series = {Deutsche Gesellschaft f{\"u}r Akustik e.V. (ed.): Fortschritte der Akustik - DAGA 2017, 43. Deutsche Jahrestagung f{\"u}r Akustik, Kiel, Deutschland}, booktitle = {Deutsche Gesellschaft f{\"u}r Akustik e.V. (ed.): Fortschritte der Akustik - DAGA 2017, 43. Deutsche Jahrestagung f{\"u}r Akustik, Kiel, Deutschland}, year = {2017}, abstract = {Ein Ziel des DFG-AiF-Clusterforschungsvorhabens Vibroakustik im Planungsprozess f{\"u}r Holzbauten ist die Reduktion des schalltechnischen Planungsaufwandes bei der Holzmassivbauweise. Hierzu soll das Prognoseverfahren der DIN EN 12354 angepasst werden. Darin ist ein vereinfachter SEA-Ansatz zugrunde gelegt, welcher diffuse K{\"o}rperschallfelder in den Bauteilen sowie deren schwache Kopplung voraussetzt. Tats{\"a}chlich trifft die Annahme diffuser K{\"o}rperschallfelder nur bei Bauteilen mit geringer Ausbreitungsd{\"a}mpfung zu. Um ein Maß f{\"u}r die Kopplung der Bauteile zu erhalten, wird ein Energieverh{\"a}ltnis aus den Bauteilschnellen gebildet. In der DIN EN ISO 10848-1 wird eine Vorschrift f{\"u}r die Messung der mittleren Bauteilschnelle beschrieben. Daraus wird ein {\"o}rtlich und zeitlich gemittelter Schnellepegel je Bauteil berechnet, mit dem die Energie des Bauteils bestimmt wird. In diesem Beitrag werden Messergebnisse von K{\"o}rperschallfeldern an Brettsperrholzelementen im Hinblick auf die Energiebestimmung diskutiert. Bei der statistischen Auswertung werden die Anzahl der Anregepositionen und der Messpositionen auf dem direkt und indirekt angeregten Bauteil analysiert. In einem weiteren Schritt werden die erforderlichen Messpositionen durch eine geeignete Auswahl reduziert und mit den pauschalen, geometrischen Vorgaben der Messvorschrift aus der DIN EN ISO 10848-1 verglichen. Die Erkenntnisse werden zur Bewertung von Messergebnissen und f{\"u}r Empfehlungen an die Durchf{\"u}hrung zuk{\"u}nftiger Messungen herangezogen.}, language = {de} } @article{MeckingScheibengraberKruseetal.2017, author = {Mecking, Simon and Scheibengraber, Markus and Kruse, Tobias and Schanda, Ulrich and Wellisch, Ulrich}, title = {Experimentally based statistical analysis of the vibrational energy of CLT building elements}, series = {24th International Congress on Sound and Vibration, ICSV24, London, United Kingdom, July 2017}, journal = {24th International Congress on Sound and Vibration, ICSV24, London, United Kingdom, July 2017}, year = {2017}, abstract = {The method is based on a simplified Statistical Energy Analysis(SEA) approach. The energy ratios of various subsystems are the main quantities to predict sound transmission. The method has proven to work sufficiently accurate for masonry and concrete buildings, where the building components like walls, floors etc. can be regarded as rather homogeneous structures. To adapt this method for solid timber constructions it is necessary to prove that basic SEA requirements are fullfilled by orthotropic materials and heterogeneous structures that occur in these building types. In a case study, an isolated T-junction formed by Cross Laminated Timber(CLT) elements is experimentally investigated. The buildings elements are subdivided in segments with typical screwed connections. In the experiments the diffusivity of the vibration field is investigated, using point excitation with a shaker at several positions and many, randomly chosen response positions. MONTE-CARLO simulations are conducted for random but fix-sized subsets of the measured response positions to find the necessary number of response positions for an accurate determination of the vibrational energy. As a result the distribution of the mean velocity levels can be approximated. In a second approach a multiple linear regression model based on the least absolute shrinkage and selection operator(LASSO)is applied, because for lower frequency bands multicollinearity is expected. In the context of linear regression modelling, data-driven methods are used to select optimal subsets of response positions, to get an estimating equation.}, language = {en} } @article{NegeleOttWellisch2018, author = {Negele, Daniel and Ott, Robert and Wellisch, Ulrich}, title = {Erfolg der Privatisierung von Krankenh{\"a}usern - Eine Einsch{\"a}tzung aus der Sicht ehemaliger {\"o}ffentlicher Krankenhaustr{\"a}ger}, series = {Gesundheits{\"o}konomie \& Qualit{\"a}tsmanagement}, journal = {Gesundheits{\"o}konomie \& Qualit{\"a}tsmanagement}, number = {1}, edition = {23}, pages = {43 -- 50}, year = {2018}, abstract = {Zielsetzung In den letzten Jahren konnte eine Steigerung der Anzahl privater Krankenh{\"a}user und Betten festgestellt werden. Die Probleme der Investitionsfinanzierung deutscher Krankenh{\"a}user in Kombination mit verst{\"a}rkenden Faktoren wie der Einf{\"u}hrung der DRGs, aber auch die Erwartung einer h{\"o}heren Effizienz nach der Privatisierung einer Klinik k{\"o}nnen als Hauptgr{\"u}nde bei Privatisierungsentscheidungen genannt werden. Es soll untersucht werden, wie ehemalige {\"o}ffentliche Krankenhaustr{\"a}ger den Erfolg der Privatisierung einsch{\"a}tzen. Methodik Alle 99 identifizierten ehemaligen {\"o}ffentlichen Tr{\"a}ger, die ihr Krankenhaus in den Jahren 2003-2013 an einen privaten Eigent{\"u}mer verkauft hatten, wurden hinsichtlich ihrer Einsch{\"a}tzung von Zielen sowie Bef{\"u}rchtungen vor und nach der Privatisierung anonym und schriftlich befragt. Ergebnisse und Schlussfolgerung Zusammenfassend l{\"a}sst sich feststellen, dass bei einer ausreichenden Finanzierung der Krankenh{\"a}user aus Krankenkassenbeitr{\"a}gen (Betriebsmittel) und L{\"a}ndermittel (Investitionskosten) der Privatisierungsdruck rapide sinken w{\"u}rde. Wenn jedoch privatisiert wurde, hatte dies aus Sicht der ehemaligen Tr{\"a}ger weder einen negativen Einfluss auf die medizinische Versorgung in der Region, noch auf die Qualit{\"a}t der medizinischen Behandlung und auf den Umgang mit den Mitarbeitern. Die Einsch{\"a}tzungen der ehemaligen Tr{\"a}ger weisen eine statistisch {\"u}berpr{\"u}fte {\"U}bereinstimmung auf. Die mit einer Privatisierung verbundenen Erwartungen und Ziele werden prinzipiell als erf{\"u}llt wahrgenommen}, language = {de} } @article{ParzingerHanfstaenglSiggetal.2020, author = {Parzinger, Michael and Hanfstaengl, Lucia and Sigg, Ferdinand and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building's Heating, Ventilation and Air Conditioning Systems}, series = {Sustainability}, volume = {12}, journal = {Sustainability}, number = {17}, pages = {6758}, year = {2020}, abstract = {Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.}, language = {en} } @article{ParzingerHanfstaenglSiggetal.2022, author = {Parzinger, Michael and Hanfstaengl, Lucia and Sigg, Ferdinand and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection — Using machine learning methods Random Forest and LASSO}, series = {Building and Environment}, volume = {223}, journal = {Building and Environment}, doi = {10.1016/j.buildenv.2022.109313}, pages = {109313}, year = {2022}, abstract = {The applications for occupancy detection range from controlling building automation and systems, determining heat transfer coefficients and even assessing the risk of infection in rooms. Studies in the literature use various statistical models, physical models and machine learning techniques to detect occupancy. All these methods require data for training the occupancy detection models. However, data generation is time-consuming and expensive. This study demonstrates the feasibility of using simulated learning data. Using three different data sources, we tested the suitability of different methods for generating learning data. We conducted two experiments in two office spaces with a real user and an artificial user, and we generated a third data set using a building simulation model. In addition, this study compares two different machine learning approaches (Random Forest and LASSO) using environmental parameters. Both machine learning approaches could develop models with a sensitivity of at least 83 \% and a specificity of at least 97 \% with both training data sets. This work shows that it is possible to determine the presence in rooms using simulated data. The results compared to measured data were just slightly less accurate, and the added value due to the lower effort was considerable}, language = {en} } @inproceedings{ParzingerWellischHanfstaengletal.2020, author = {Parzinger, Michael and Wellisch, Ulrich and Hanfstaengl, Lucia and Sigg, Ferdinand and Wirnsberger, Markus and Spindler, Uli}, title = {Identifying faults in the building system based on model prediction and residuum analysis}, series = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, volume = {172}, booktitle = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, pages = {22001}, year = {2020}, abstract = {The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.}, language = {en} } @techreport{PruschkaWellisch1997, author = {Pruschka, H. and Wellisch, Ulrich}, title = {Asymptotic behaviour of estimation equations with functional nuisance or working parameter.}, year = {1997}, abstract = {We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing with estimation equations which have a parametric component of interest and a functional (nonparametric) nuisance component. We give sufficient conditions for the existence and the asymptotic normality of a consistent estimation equation estimator for the parameter of interest. These conditions concern the asymptotic distribution of the estimation function and of its derivative as well as the effect of the functional nuisance part in the estimation equation. In order to treat the nonparametric component we introduce a general differential calculus and a general mean value theorem. For the nonparametric part in the estimation equation we distinguish two cases: the situation of a (classical) nuisance parameter and the case of a so called working parameter. As a special case we get regularity conditions for estimation equations with finite dimensional nuisance or working parameter. As an example we present the semiparametric linear regression model.}, language = {en} } @techreport{SandorSchalkSussmannetal.2016, author = {Sandor, V. and Schalk, D. and Sussmann, M. and Wellisch, Ulrich}, title = {Statistische Datenanalyse und Modellierung - Einsatzm{\"o}glichkeiten von Machine Learning Verfahren. Forschungsbericht 2016 Hochschule Rosenheim}, year = {2016}, language = {de} } @techreport{SandorSchalkSussmannetal.2016, author = {Sandor, V. and Schalk, D. and Sussmann, M. and Wellisch, Ulrich}, title = {Statistische Datenanalyse und Modellierung - Machine Learning in der Versicherung, Risikomodelle auf dem Pr{\"u}fstand. Forschungsbericht 2016 Hochschule Rosenheim}, year = {2016}, language = {de} } @techreport{SebaldSussmannSandoretal.2015, author = {Sebald, D. and Sussmann, M. and Sandor, V. and Wellisch, Ulrich}, title = {Statistische Datenanalyse und Modellierung - Risikomodellierung in der Krafthaftpflichtversicherung. Forschungsbericht Hochschule Rosenheim 2015}, year = {2015}, language = {de} }