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Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.


Papers
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Book ChapterDOI
09 Nov 2011
TL;DR: In this paper, the authors tackle the topics of robustness and multivariate outlier detection for ordinal data and illustrate how to detect atypical measurements in customer satisfaction surveys.
Abstract: This chapter tackles the topics of robustness and multivariate outlier detection for ordinal data. We initially review outlier detection methods in regression for continuous data and give an example which shows that graphical tools of data analysis or traditional diagnostic measures based on all the observations are not sufficient to detect multivariate atypical observations. Then we focus on ordinal data and illustrate how to detect atypical measurements in customer satisfaction surveys. Next, we review the generalized linear model of ordinal regression and apply it to the ABC survey. The chapter concludes with an analysis of a set of diagnostics to check the goodness of the suggested model and the presence of anomalous observations.

14 citations

Posted Content
TL;DR: This work proposes a learning-based approach to autofocus, and provides a realistic dataset of sufficient size for effective learning, and demonstrates that this approach provides a significant improvement compared with previous learned and non-learned methods.
Abstract: Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, following "Learning single camera depth estimation using dual-pixels". Using this dataset, we apply modern deep classification models and an ordinal regression loss to obtain an efficient learning-based autofocus technique. We demonstrate that our approach provides a significant improvement compared with previous learned and non-learned methods: our model reduces the mean absolute error by a factor of 3.6 over the best comparable baseline algorithm. Our dataset and code are publicly available.

14 citations

Journal ArticleDOI
TL;DR: Evidence is presented showing that selected tests of significance and the correlation coefficient are chosen so that they are essentially unaffected by nonlinear, order-preserving transformations of the data.
Abstract: This paper has been prompted by two recent additions (Labovitz, 1967, 1970a) to the continuing controversy over levels of measurement and permissible statistics. In these articles Labovitz urges us to treat ordinal and \"quasi-interval\" data (data scaled between the ordinal and interval level) as though they were scaled at the interval evel. The justification for this position is the pragmatic argument hat little error is introduced into the analysis by doing this, since empirical analyses have shown a number of statistics to be essentially unaffected by nonlinear, order-preserving transformations of the data. To this end he presents evidence showing that selected tests of significance (1967) and the correlation coefficient (1970a) are

14 citations

Proceedings ArticleDOI
19 Sep 2021
TL;DR: A novel preprocessing method for reducing the sparseness and limited field of view provided by radar and a novel method for estimating dense depth maps from monocular 2D images and sparse radar measurements using deep learning based on the deep ordinal regression network are proposed.
Abstract: We integrate sparse radar data into a monocular depth estimation model and introduce a novel preprocessing method for reducing the sparseness and limited field of view provided by radar. We explore the intrinsic error of different radar modalities and show our proposed method results in more data points with reduced error. We further propose a novel method for estimating dense depth maps from monocular 2D images and sparse radar measurements using deep learning based on the deep ordinal regression network by Fu et al. Radar data are integrated by first converting the sparse 2D points to a height-extended 3D measurement and then including it into the network using a late fusion approach. Experiments are conducted on the nuScenes dataset. Our experiments demonstrate state-of-the-art performance in both day and night scenes.

14 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined discharge-related anxiety in a group of 65 patients resident in five medium secure units located in the South of England and found that the main predictors of a general dischargerelated anxiety scale were low self-esteem and perceived absence of social support, although high trait anxiety also exerted a significant independent effect.
Abstract: This study examines discharge-related anxiety in a group of 65 patients resident in five medium secure units located in the South of England. The study is part of a larger investigation of non-compliance within medium secure unit environments. Participants completed standardised questionnaire measures of self-efficacy, self-esteem, anxiety and locus of control, together with a newly constructed questionnaire investigating anxiety relating to discharge. Results of ordinal regression procedures indicated that the main predictors of a general discharge-related anxiety scale were low self-esteem and perceived absence of social support, although on univariate analysis high trait anxiety also exerted a significant independent effect. The clinical implications of the findings are discussed.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023102
2022191
202188
202093
201979
201873