Andreas Groll (13 results)

Condition: gut. 236 S. : graph. Darst. ; 21 cm, Stempel. Sprache: Deutsch.

Softcover/Paperback. 1. Auflage. 271 S. mit Abb. / etw. schief gelegen, Einband etw. bestaubt // Fotografie N08 9783902993205 *.* Sprache: Englisch Gewicht in Gramm: 1450.

Condition: gut. Antibiotika-Therapie: Klinik und Praxis der antiinfektiösen Behandlung In deutscher Sprache. pages.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A regression analysis describes the dependency of random variables in the form of a functional relationship. One distinguishes between the dependent response variable and one or more independent influence variables. There is a variety of model classes and inference methods available, ranging from the conventional linear regression model up to recent non- and semiparametric regression models. The so-called generalized regression models form amethodically consistent framework incorporating many regression approaches with response variables that are not necessarily normally distributed, including the conventional linear regression model based on the normal distribution assumption as a special case. When repeated measurements are modeled in addition to fixed effects also random effects or coefficients can be included. Such models are known as Random Effects Models or Mixed Models. As a consequence, regression procedures are applicable extremely versatile and consider very different problems. In this dissertation regularization techniques for generalized mixed models are developed that are able to perform variable selection. These techniques are especially appropriate when many potential influence variables are present and existing approaches tend to fail. First of all a componentwise boosting technique for generalized linear mixed models is presented which is based on the likelihood function and works by iteratively fitting the residuals using weak learners. The complexity of the resulting estimator is determined by information criteria. For the estimation of variance components two approaches are considered, an estimator resulting from maximizing the profile likelihood, and an estimator which can be calculated using an approximative EM-algorithm. Then the boosting concept is extended to mixed models with ordinal response variables. Two different types of ordered models are considered, the threshold model, also known as cumulative model, and the sequential model. Both are based on the assumption that the observed response variable results from a categorized version of a latent metric variable. In the further course of the thesis the boosting approach is extended to additive predictors. The unknown functions to be estimated are expanded in B-spline basis functions, whose smoothness is controlled by penalty terms. Finally, a suitable L1-regularization technique for generalized linear models is presented, which is based on a combination of Fisher scoring and gradient optimization. Extensive simulation studies and numerous applications illustrate the competitiveness of the methods constructed in this thesis compared to conventional approaches. For the calculation of standard errors bootstrap methods are used. 176 pp. Englisch.

Wien, undated ( before 1869 ), the 8 albumine prints are mounted on 6 boards. The albumine prints represent different views of the original drawings by von Sicardsburg and van der Nüll. Sizes vary between 26 x 38 cm and 50 x 20 cm. Some of the drawings are titled. E.g. ''Rückwärtige Ausicht'' on board n°6. (This is also the print with Groll's name). Other drawings are: Groundfloor, Querschnitt durch die Bühne, Querschnitt, Seitenansicht (aussen). .All prints are mounted on contemporary boards. (62 x 46 cm). The boards are a bit dustsoiled, some of the prints are more or less faded. The boards are stamped with the collection stamp of Frans Baeckelmans ( 1835 - 1896) . He was an Antwerp (Belgium - Flanders) architect who reputedly designed neo-renaissance buildings partially based on photographic examples of classical renaissance buildings ( e.g. the old court of justice in Antwerp, based on a Baldus picture of the Louvre - this picture also formed part of his collection). The photographer Andreas Groll ( 1812 - 1872) was one of the photography pioneers of the Habsburg empire. Architecture was one of his main intrests. The Wiener Staatsoper ( still standing) was contructed between 1861 and 1869 at the newly constructed Ringstrasse. Its neo-renaissance style must surely greatly have pleased the Antwerp architect collector . This collection is an interesting and early testimony of the use of the newly invention of the art of photography in architectural design.

um 1860, Albuminabzug auf Karton, signiert und nummeriert im Negativ "Groll / A / 4", Größe 23,8 x 12,7 cm Andreas Groll (1812-1872) war Fotograf, Daguerreotypist ab 1843 und 1846-1856 Laborant bei Anton Martin. Er zählt zu den wichtigen Architekturfotografen und fotografierte für Eduard Freiherr von Sackens sämtliche Ouevres für die Sammelbände "Rüstungen und Waffen der k. k. Ambraser Sammlung" (ab 1857) und "Kunstwerke und Geräthe des Mittelalters und der Renaissance in der k. k. Ambraser Sammlung" (ab 1864). Abgebildet im Katalog "Gipsmodell und Fotografie im Dienste der Kunstgeschichte 1850 - 1900", Simon Weber-Unger, S. 98, No. 144.

um 1860, Albuminabzug auf Karton, Größe 22,5 x 23,6 cm Andreas Groll (1812-1872) war Fotograf, Daguerreotypist ab 1843 und 1846-1856 Laborant bei Anton Martin. Er zählt zu den wichtigen Architekturfotografen und fotografierte für Eduard Freiherr von Sackens sämtliche Ouevres für die Sammelbände "Rüstungen und Waffen der k. k. Ambraser Sammlung" (ab 1857) und "Kunstwerke und Geräthe des Mittelalters und der Renaissance in der k. k. Ambraser Sammlung" (ab 1864). Abgebildet im Katalog "Gipsmodell und Fotografie im Dienste der Kunstgeschichte 1850 - 1900", Simon Weber-Unger, S. 99, No. 145.

Farben-Lichtdruck (Öldruck) bei J. Loewy, Druck & Verlag der Gesellschaft für vervielf. Kunst, Wien, 1885, 25x13 cm, auf Unterlagskarton montiert.

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnA regression analysis describes the dependency of random variables in the form of a functional relationship. One distinguishes between the dependent response variable and one or more independent influence variables. There is a var.

8°. Verso altem Trägerkarton ungelenk beschriftet: Zigeuner bei Reschitza (Geburtsort von Julius Meier-Graefe), in der Literatur bekannt als Zigeunerlager im Banat. Vgl. Faber, M. Industriefotografie S. 117 mit Abbildung.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -A regression analysis describes the dependency of random variables in the form of a functional relationship. One distinguishes between the dependent response variable and one or more independent influence variables. There is a variety of model classes and inference methods available, ranging from the conventional linear regression model up to recent non- and semiparametric regression models. The so-called generalized regression models form amethodically consistent framework incorporating many regression approaches with response variables that are not necessarily normally distributed, including the conventional linear regression model based on the normal distribution assumption as a special case. Whenrepeated measurements are modeled in addition to fixed effects also random effects or coefficients can be included. Such models are known as Random Effects Models or Mixed Models. As a consequence, regression procedures are applicable extremely versatile andconsider very different problems.In this dissertation regularization techniques for generalized mixed models are developed that are able to perform variable selection. These techniques are especially appropriate when many potential influence variables are present and existing approaches tend to fail. First of all a componentwise boosting technique for generalized linear mixed models is presented which is based on the likelihood function and works by iteratively fitting the residuals using weak learners. The complexity of the resulting estimator is determined by information criteria. For the estimation of variance components two approaches are considered, an estimator resultingfrom maximizing the profile likelihood, and an estimator which can be calculated using an approximative EM-algorithm. Then the boosting concept is extended to mixed models with ordinal response variables. Two different types of ordered models are considered, the threshold model, also known as cumulative model, and the sequential model. Both are based on the assumption that the observed response variable results from a categorized version of a latent metric variable. In the further course of the thesis the boosting approach is extendedto additive predictors. The unknown functions to be estimated are expanded in B-spline basis functions, whose smoothness is controlled by penalty terms. Finally, a suitable L1-regularization technique for generalized linear models is presented, which is based on acombination of Fisher scoring and gradient optimization. Extensive simulation studies and numerous applications illustrate the competitiveness of the methods constructed in this thesis compared to conventional approaches. For the calculation of standard errors bootstrap methods are used.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 176 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A regression analysis describes the dependency of random variables in the form of a functional relationship. One distinguishes between the dependent response variable and one or more independent influence variables. There is a variety of model classes and inference methods available, ranging from the conventional linear regression model up to recent non- and semiparametric regression models. The so-called generalized regression models form amethodically consistent framework incorporating many regression approaches with response variables that are not necessarily normally distributed, including the conventional linear regression model based on the normal distribution assumption as a special case. When repeated measurements are modeled in addition to fixed effects also random effects or coefficients can be included. Such models are known as Random Effects Models or Mixed Models. As a consequence, regression procedures are applicable extremely versatile and consider very different problems. In this dissertation regularization techniques for generalized mixed models are developed that are able to perform variable selection. These techniques are especially appropriate when many potential influence variables are present and existing approaches tend to fail. First of all a componentwise boosting technique for generalized linear mixed models is presented which is based on the likelihood function and works by iteratively fitting the residuals using weak learners. The complexity of the resulting estimator is determined by information criteria. For the estimation of variance components two approaches are considered, an estimator resulting from maximizing the profile likelihood, and an estimator which can be calculated using an approximative EM-algorithm. Then the boosting concept is extended to mixed models with ordinal response variables. Two different types of ordered models are considered, the threshold model, also known as cumulative model, and the sequential model. Both are based on the assumption that the observed response variable results from a categorized version of a latent metric variable. In the further course of the thesis the boosting approach is extended to additive predictors. The unknown functions to be estimated are expanded in B-spline basis functions, whose smoothness is controlled by penalty terms. Finally, a suitable L1-regularization technique for generalized linear models is presented, which is based on a combination of Fisher scoring and gradient optimization. Extensive simulation studies and numerous applications illustrate the competitiveness of the methods constructed in this thesis compared to conventional approaches. For the calculation of standard errors bootstrap methods are used.

Taschenbuch. Condition: Neu. Variable Selection by Regularization Methods for Generalized Mixed Models | Andreas Groll | Taschenbuch | Englisch | 2011 | Cuvillier | EAN 9783869559636 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.