Refine
Year of publication
- 2024 (135)
- 2023 (258)
- 2022 (303)
- 2021 (293)
- 2020 (245)
- 2019 (385)
- 2018 (262)
- 2017 (262)
- 2016 (284)
- 2015 (301)
- 2014 (302)
- 2013 (301)
- 2012 (333)
- 2011 (329)
- 2010 (343)
- 2009 (369)
- 2008 (312)
- 2007 (312)
- 2006 (330)
- 2005 (303)
- 2004 (323)
- 2003 (254)
- 2002 (250)
- 2001 (221)
- 2000 (245)
- 1999 (236)
- 1998 (242)
- 1997 (220)
- 1996 (202)
- 1995 (192)
- 1994 (175)
- 1993 (155)
- 1992 (144)
- 1991 (100)
- 1990 (108)
- 1989 (111)
- 1988 (104)
- 1987 (105)
- 1986 (81)
- 1985 (84)
- 1984 (75)
- 1983 (70)
- 1982 (57)
- 1981 (54)
- 1980 (61)
- 1979 (58)
- 1978 (52)
- 1977 (32)
- 1976 (30)
- 1975 (28)
- 1974 (17)
- 1973 (12)
- 1972 (17)
- 1971 (11)
- 1970 (2)
- 1969 (2)
- 1968 (2)
- 1967 (1)
- 1963 (1)
- 1925 (1)
Institute
- Fachbereich Medizintechnik und Technomathematik (2077)
- Fachbereich Elektrotechnik und Informationstechnik (1184)
- Fachbereich Wirtschaftswissenschaften (1164)
- Fachbereich Energietechnik (1115)
- Fachbereich Chemie und Biotechnologie (914)
- Fachbereich Maschinenbau und Mechatronik (876)
- Fachbereich Luft- und Raumfahrttechnik (798)
- Fachbereich Bauingenieurwesen (711)
- IfB - Institut für Bioengineering (690)
- INB - Institut für Nano- und Biotechnologien (617)
Language
- German (5133)
- English (4929)
- Russian (14)
- Portuguese (6)
- Italian (5)
- Multiple languages (5)
- Spanish (3)
- Dutch (2)
Document Type
- Article (5658)
- Conference Proceeding (1650)
- Book (1083)
- Part of a Book (571)
- Bachelor Thesis (327)
- Patent (176)
- Report (102)
- Doctoral Thesis (80)
- Other (76)
- Administrative publication (76)
Keywords
- Amtliche Mitteilung (71)
- Bachelor (33)
- Aachen University of Applied Sciences (31)
- Master (31)
- Prüfungsordnung (31)
- Bauingenieurwesen (30)
- Lesbare Fassung (28)
- Biosensor (25)
- Fachhochschule Aachen (23)
- Illustration (23)
Zugriffsart
- campus (2151)
- weltweit (1884)
- bezahl (773)
- fachbereichsweit (FB4) (34)
This paper considers a paired data framework and discusses the question of marginal homogeneity of bivariate high-dimensional or functional data. The related testing problem can be endowed into a more general setting for paired random variables taking values in a general Hilbert space. To address this problem, a Cramér–von-Mises type test statistic is applied and a bootstrap procedure is suggested to obtain critical values and finally a consistent test. The desired properties of a bootstrap test can be derived that are asymptotic exactness under the null hypothesis and consistency under alternatives. Simulations show the quality of the test in the finite sample case. A possible application is the comparison of two possibly dependent stock market returns based on functional data. The approach is demonstrated based on historical data for different stock market indices.
With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Application of polymers in textile reinforced concrete : from the interface to construction elements
(2006)
Composite improvement of textile reinforced concrete by polymeric impregnation of the textiles
(2006)
Mikrokleben
(2005)
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
Based on an identifying Volterra type integral equation for randomly right censored observations from a lifetime distribution function F, we solve the corresponding estimating equation by an explicit and implicit Euler scheme. While the first approach results in some known estimators, the second one produces new semi-parametric and pre-smoothed Kaplan–Meier estimators which are real distribution functions rather than sub-distribution functions as the former ones are. This property of the new estimators is particular useful if one wants to estimate the expected lifetime restricted to the support of the observation time.
Specifically, we focus on estimation under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. We show that some estimated linear functionals which are based on the new semi-parametric estimator are strong consistent, asymptotically normal, and efficient under SRCM. In a small simulation study, the performance of the new estimator is illustrated under moderate sample sizes. Finally, we apply the new estimator to a well-known real dataset.