Stefan Lang

Ludwig Fahrmeir

Thomas Kneib

Brian D. Marx

Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book. The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

Models, Methods and Applications

Regression

Peter C. Lockemann

Datenbanken spielen als Informationsspeicher immer mehr eine zentrale Rolle. Daher müssen ihr erfolgreicher Aufbau und ihr fehlerfreier Betrieb langfristig gesichert sein. Umfassende Kenntnisse der verwendeten Modelle, der verschiedenen Entwurfstechniken sowie der beim laufenden Betrieb zu beachtenden Maßregeln sind dazu notwendig. Das Buch widmet sich diesen Themen vor allem unter dem Aspekt der Datenbanknutzung. So kann der Praktiker die behandelten Konzepte und Techniken sofort anwenden. Ebenso bietet es Studierenden und Wissenschaftlern fundierte Information über aktuelle Techniken und Trends (verteilte datenintensive Anwendungen, Migration auf moderne Plattformen, Standardisierung). Drei durchgängige Anwendungsbeispiele ermöglichen den Vergleich unterschiedlicher Lösungsmethoden.

Datenbankeinsatz

Brian Marx

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Zusammenfassung aus dem Jahr 2016 im Fachbereich Psychologie - Sonstiges, Note: 1,3, FOM Hochschule für Oekonomie & Management gemeinnützige GmbH, München früher Fachhochschule, Sprache: Deutsch, Abstract: Die vorliegende Arbeit bietet einen Überblich zum Thema Emotionen und Gefühle und greift dazu die Theorien von Carroll Izard und Robert Plutchik auf. Aus dem Inhalt: - Unterschied zwischen Gefühl und Emotion; - Emotionskonzept von Carroll Izard; - Emotionskomplexität von Robert Plutchik;

Gefühle oder Emotionen? Emotionskonzepte von Carroll Izard und Robert Plutchik

Thomas Blaschke

Geoffrey Hay

This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

Stefan Lang

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