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Anja Vogler

Bio: Anja Vogler is an academic researcher. The author has contributed to research in topics: Multivariate analysis. The author has an hindex of 1, co-authored 1 publications receiving 3066 citations.

Papers
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01 Jan 2016
TL;DR: The introduction to multivariate statistical analysis is universally compatible with any devices to read, and will help you to cope with some harmful bugs inside their desktop computer.
Abstract: Thank you for downloading introduction to multivariate statistical analysis. Maybe you have knowledge that, people have look hundreds times for their favorite books like this introduction to multivariate statistical analysis, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their desktop computer. introduction to multivariate statistical analysis is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the introduction to multivariate statistical analysis is universally compatible with any devices to read.

3,304 citations


Cited by
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Journal ArticleDOI
Abstract: (2003). Applied Multivariate Statistics for the Social Sciences. The American Statistician: Vol. 57, No. 1, pp. 68-69.

7,141 citations

Journal ArticleDOI
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

3,894 citations

Journal ArticleDOI
TL;DR: A general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis is presented that clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models.
Abstract: Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables.

3,624 citations

Journal ArticleDOI
TL;DR: This review describes for the practitioner the essential features of line‐fitting methods for estimating the relationship between two variables: what methods are commonly used, which method should be used when, and how to make inferences from these lines to answer common research questions.
Abstract: Fitting a line to a bivariate dataset can be a deceptively complex problem, and there has been much debate on this issue in the literature. In this review, we describe for the practitioner the essential features of line-fitting methods for estimating the relationship between two variables: what methods are commonly used, which method should be used when, and how to make inferences from these lines to answer common research questions. A particularly important point for line-fitting in allometry is that usually, two sources of error are present (which we call measurement and equation error), and these have quite different implications for choice of line-fitting method. As a consequence, the approach in this review and the methods presented have subtle but important differences from previous reviews in the biology literature. Linear regression, major axis and standardised major axis are alternative methods that can be appropriate when there is no measurement error. When there is measurement error, this often needs to be estimated and used to adjust the variance terms in formulae for line-fitting. We also review line-fitting methods for phylogenetic analyses. Methods of inference are described for the line-fitting techniques discussed in this paper. The types of inference considered here are testing if the slope or elevation equals a given value, constructing confidence intervals for the slope or elevation, comparing several slopes or elevations, and testing for shift along the axis amongst several groups. In some cases several methods have been proposed in the literature. These are discussed and compared. In other cases there is little or no previous guidance available in the literature. Simulations were conducted to check whether the methods of inference proposed have the intended coverage probability or Type I error. We identified the methods of inference that perform well and recommend the techniques that should be adopted in future work.

1,952 citations

Journal ArticleDOI
TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.

1,902 citations