Podcasts about fahrmeir

Wir sprechen heute mit Helge Wendt. Dieser hat Geschichte und Altamerikanistik studiert und hat ganz aktuell seine Habilitation “Kohlezeit. Eine Global- und Wissensgeschichte (1500–1900) rausgebracht. Darüber sprechen wir auch hauptsächlich mit ihm. Am Anfang geht es dabei viel um den Ansatz, die Theorien und Methoden: Wir sprechen darüber was Globalgeschichte überhaupt ist und was nicht; und warum jede Geschichte so regional sie auch sein mag, auch Globalgeschichte ist. Doch Helge Wendt spricht mit uns auch über die inhaltlichen Erkenntnisse aus seiner Recherche und beschreibt was sich durch Kohle als Sonde darstellen und zeigen lässt. Warum ist Kohlezeit mit der Industrialisierung verbunden? Welche Bedenken im Bezug auf die Umwelt gab es bereits im 18. und 19. Jahrhundert? Zum Schluss thematisieren wir auch die Entwicklungen in der Gegenwart und die Diskurse rund um die Energiewende. Wer Gast sein möchte, Fragen oder Feedback hat, kann dieses gerne an houseofmodernhistory@gmail.com oder auf Twitter an richten. Literatur & Quellen: Blog Helge Wendt: https://helgewendt.com/ Boucheron, Patrick: Histoire mondiale de la France. 2017. Brüggemeier, Franz J.: Grubengold. Das Zeitalter der Kohle von 1750 bis heute. C.H. Beck, 2018. Chakrabarty, Dipesh: Europa als Provinz. Perspektiven postkolonialer Geschichtsschreibung. Frankfurt am Main, 2010. DAAD Stipendien: https://www.daad.de/de/studieren-und-forschen-in-deutschland/stipendien-finden/ DFG Stoffgeschichte: http://stoffgeschichte.org/ Drach, Albert: Werke in zehn Bänden. Paul Zsolnay Verlag, Wien 2002ff. h-net: https://www.h-net.org/ Fahrmeir, Andreas (Hg.): Deutschland: Globalgeschichte einer Nation. C.H. Beck, 2020. Thorade, Nora: Das Schwarze Gold. Eine Stoffgeschichte der Kohle im 19. Jahrhundert. Brill/Schöningh, 2020. Renn, J., Laubichler, M. D., & Wendt, H. (2015). Energietransformationen zwischen Kaffee und Koevolution. In N. Möllers, C. Schwägerl, & H. Trischler (Eds.): Willkommen im Anthropozän: unsere Verantwortung für die Zukunft der Erde; Katalog zur Sonderausstellung am Deutschen Museum (pp. 79-82). Munich: Deutsches Museum. Portada del Archivo General de Indias: https://www.culturaydeporte.gob.es/cultura/areas/archivos/mc/archivos/agi/portada.html Steffen, Will: Introducing the Anthropocene: The human epoch. Ambio 50, 1784–1787, 2021. Steffen, Will, et al: The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature? Ambio, 36: 8, 2007, pp. 614–21. JSTOR, http://www.jstor.org/stable/25547826 Wendt, Helge: Kohlezeit. Eine Global- und Wissensgeschichte (1500-1900). Frankfurt/New York: Campus Verlag, 2021. Wendt, Helge: Die missionarische Gesellschaft. Mikrostrukturen einer kolonialen Globalisierung. Stuttgart, 2011. Wendt, Helge: Kohlebäume: Darstellungen von Industrie und Wissenschaft in politischen Dimensionen (1900-1945). clio online, 2023: https://www.europa.clio-online.de/sites/europa.clio-online/files/documents/B2023/E_Wendt_Kohlebaeume.pdf Zalasiewicz, J. et al. (2015) When did the Anthropocene begin? A mid-twentieth century boundary level is stratigraphically optimal. Quaternary international.

Andreas Fahrmeir (Hg.): "Deuschland. Globalgeschichte einer Nation" | C.H. Beck Verlag 2020 | Preis: 39,95 Euro

In 177 kurzen Beiträgen bettet dieser Sammelband zentrale Ereignisse der deutschen Geschichte in ihren globalhistorischen Kontext ein. Rezension von Konstantin Sakkas. C.H. Beck Verlag 2020 ISBN 978-3-406-75619-1 936 Seiten 39,95 Euro

Bei der stochastischen Analyse von Kaufverhalten konzentriert man sich besonders auf die Aspekte des Kaufzeitpunkts, der Produktwahl und der Kaufmenge, um im Wettbewerb einen Vorteil gegenüber der Konkurrenz zu erhalten. Kristina Cindric führt im Gespräch mit Gudrun Thäter aus, wie sie auf Basis großer Datenmengen von anonymisierten Vertragsabschlüssen über ein Jahr Analysen erstellt, Modelle entworfen, trainiert und die entstehenden Prognosen getestet hat. Die auftretenden stochastischen Modelle können sehr vielseitig sein: So kann der Kaufzeitpunkt beispielsweise durch einen stochastischen Prozess mit exponentieller Verteilung modelliert werden, für die Produktwahl kann ein Markow-Prozess die wahrscheinlichsten nächsten Käufe abbilden. Ein zentrales Konzept für die Analyse und das Training von Modellen ist dann die Parameterschätzung, die die tatsächliche Ausgestaltung der Modelle aus den Daten bestimmt. Literatur und Zusatzinformationen L. Fahrmeir, G. Raßer, T. Kneib: Stochastische Prozesse, Institut für Statistik, Ludwig-Maximilians-Universität München, 2010. O. C. Ibe: Markov Processes for Stochastic Modeling, Academic Press, Amsterdam, 2009. L. Yan, R. H. Wolniewicz, R. Dodier: Predicting Customer Behavior in Telecommunications, IEEE Computer Society 19, 50-58, 2004. J. Xia, P. Zeephongsekul, D. Packer: Spatial and temporal modelling of tourist movements using Semi-Markov processes, Tourism Management 32, 844-851, 2010. C. Ebling, Dynamische Aspekte im Kaufverhalten: Die Determinanten von Kaufzeitpunkt, Marken- und Mengenwahl, Inaugural-Dissertation, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2007. Forschungszentrum Informatik (FZI)

The objective of this work is to examine the impact of socioeconomic and public health factors on childhood diseases and malnutrition in mentioned countries. The causes of child's illness or child's undernutrition are multiple. This work focuses on some risk factors which are assumed to cause the child's diseases and malnutrition as suggested by some previous works (see Kandala, 2001; Adebayo, 2002). Our analysis started with a large number of covariates including a set of bio-demographic and socioeconomic variables, such as current working status of mothers, place of residence, access to toilet facilities, etc (see chapter 2). The analyses are based on data from the 2003 household survey for Egypt and Nigeria for the Demographic and Health Surveys (DHS). More details about the data set are mentioned in the first chapter. The statistical analysis in this thesis is based on modern Bayesian approaches which allow a flexible framework for realistically complex models. These models allow us to analyze usual linear effects of categorical covariates, nonlinear effects of continuous covariates and the geographical effects within a unified semi-parametric Bayesian framework for modelling and inference. A first step of this work is to analyze the effects of the different types of covariates on response variables, diarrhea, fever, and cough which represent the child's diseases in our application. In this step, a Bayesian geoadditive logit model for binary response variables is used (see Fahrmeir and Lang, 2001). In a second step, we employ separate geoadditive probit models (instead of logit models used in the previous step) to the binary listed variables. Based on the results of the separate analyses, we applied geoadditive latent variable probit models (recently suggested by Raach, 2005; Raach and Fahrmeir, 2006) where the three observable disease variables are assumed to be indicators for the latent variable "health status" for the children. In this step, we also compared the results of the separate geoadditive probit models with the results of the latent variable models. As a third step, we used geoadditive Gaussian regression and latent variable models to analyze the malnutrition status of children in both countries. Finally, we used latent variable models for diseases and nutrition indicators together. In the final step, models with one as well as with two latent variables have been estimated using mixed indicators (binary indicators "health status", and continuous indicators "nutrition status") and the results are compared.

Wed, 13 Jun 2007 12:00:00 +0100 https://edoc.ub.uni-muenchen.de/7978/ https://edoc.ub.uni-muenchen.de/7978/1/Fahrmeir_Julia.pdf Fahrmeir, Julia ddc:500, ddc:540, Fakultät für Chemie und

This technical report acts as a supplement to the paper "Structured additive regression for categorical space-time data: A mixed model approach" (Kneib and Fahrmeir, Biometrics, 2005, to appear). Details on several specific models for categorical responses are given as well as a description on how to construct design matrices in structured additive regression models. Furthermore some technical information on inferential issues and additional results from the simulation studies are provided. To ease orientation, sections in the supplement are named in analogy to the sections in the original paper. Also, formulas are presented with the same numbers.

This technical report supplements the paper Geoadditive Survival Models (Hennerfeind, Brezger and Fahrmeir, 2005, Revised for JASA). In particular, we describe the simulation study of this paper in greater detail, present additional results for the application, and provide a complete proof of Theorem 1, Corollary 1, as well as the lemmata and corollaries in the appendix.

Generalized additive mixed models extend the common parametric predictor of generalized linear models by adding unknown smooth functions of different types of covariates as well as random effects. From a Bayesian viewpoint, all effects as well as smoothing parameters are random. Assigning appropriate priors, posterior inference can be based on Markov chain Monte Carlo techniques within a unified framework. Given observations on the response and on covariates, questions like the following arise: Can the additive structure be recovered? How well are unknown functions and effects estimated? Is it possible to discriminate between different types of random effects? The aim of this paper is to obtain some answers to such questions through a careful simulation study. Thereby, we focus on models for Gaussian and categorical responses based on smoothness priors as in Fahrmeir and Lang (2001). The result of the study provides valuable insight into the facilities and limitations of the models when applying them to real data.

Most methods for analyzing failure time or event history data are based on time as a continuously measured variate. A basic assumption for large parts of theory is that failure times are untied, see Andersen et al. (1993). In practice, there is always some smallest time unit, so that ties can occur. A moderate number of ties, while banned in theory, can be treated by appropriate modifications. If many ties occur, e.g. due to grouping in larger time units or intervals, or if time is truly discrete, then discrete survival or failure time models are more consistent with the data. Such situations arise in medical work when patients are followed up at fixed intervals like months, in certain biostatistical problems, for example human fertility studies and time to pregnancy (Scheike and Jensen, 1997), or in labor market studies where duration of unemployment is measured in weeks, at best, or in months. We review parametric models and outline recent nonparametric approaches. More details, in particular for parametric models, are given e.g. in Fahrmeir and Tutz (1994), ch. 9, and further references cited there.

Data-driven hyperparameter estimation or automatic choice of the smoothing parameter is of great importance, especially in the applications. This article presents and compares three methods for hyperparameter estimation in the framework of exponential family state space models: First, we motivate and derive a formula for an approximative likelihood, and an alternative, yet mathematical equivalent, expression proves to be a generalized version of a proposal in Durbin and Koopman (1992). Second, the EM-type algorithm suggested in Fahrmeir (1992) is restated here for reasons of comparison and third, the idea of cross-validation proposed by Kohn and Ansley (1989) for linear state space models is extended to the present context, in particular for multicategorical and multidimensional responses. Finally, we compare the three methods for hyperparameter estimation by applying each on three real data sets.