Results 221 to 230 of about 82,789 (258)

Segmented Linear Regression Trees

Acta Mathematica Sinica, English Series
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, Xiangyu, Chen, Songxi
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Segmented relationships to model erosion of regression effect in Cox regression

Statistical Methods in Medical Research, 2010
In this article we propose a parsimonious parameterisation to model the so-called erosion of the covariate effect in the Cox model, namely a covariate effect approaching to zero as the follow-up time increases. The proposed parameterisation is based on the segmented relationship where proper constraints are set to accomodate for the erosion.
MUGGEO, Vito Michele Rosario, ATTANASIO, Massimo   +1 more
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Segmented Cox Regression (60 Patients)

2012
Cox regression assesses time to events, like death or cure, and the effects of predictors like comorbidity and frailty. If a predictor is not significant, then time-dependent Cox regression may be a relevant approach. It assesses whether the predictor interacts with time. Time dependent Cox has been explained in Chap. 56.
Ton J. Cleophas, Aeilko H. Zwinderman
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A fuzzy clusterwise regression approach to benefit segmentation

International Journal of Research in Marketing, 1989
Abstract An algorithm for fuzzy clusterwise regression (FCR) is proposed which can be used for benefit segmentation within the framework of preference analysis. The method simultaneously estimates the models relating preference to product dimensions within each cluster, as well as the parameters indicating the degree of membership of subjects in ...
Wedel, M., Steenkamp, J.E.B.M.
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Subspace Segmentation by Correlation Adaptive Regression

IEEE Transactions on Circuits and Systems for Video Technology, 2018
Subspace segmentation aims to segment a given data set into clusters with each cluster corresponding to a subspace. Most recent works focus on subspace representation-based methods, which construct the affinity matrix based on the subspace representation of the data points.
Weiwei Wang, Binbin Zhang, Xiangchu Feng
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Boundary Regression for Human Vertebrae Segmentation

2019 9th International Conference on Advances in Computing and Communication (ICACC), 2019
The diagnosis and treatment of pathologies like Low back pain, Osteoporosis, Spondyflolisthesis etc. require detailed analysis of spinal images. Manual segmentation of vertebrae in spinal images is difficult. In this paper we discuss an automatic method for segmentation of vertebrae.
M.A Ancy Brigit, Dominic Mathew, Abraham Thomas   +2 more
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Causal Segment Regression with Multiple Thresholds

Statistics and Computing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Applications of segmented regression models for biomedical studies

American Journal of Physiology-Endocrinology and Metabolism, 1996
In many biological models, a relationship between variables may be modeled as a linear or polynomial function that changes abruptly when an independent variable obtains a threshold level. Usually, the transition point is unknown, and a major objective of the analysis is its estimation. This type of model is known as a segmented regression model.
N G, Berman, W K, Wong, S, Bhasin, E, Ipp   +3 more
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Deep feature regression (DFR) for 3D vessel segmentation

Physics in Medicine & Biology, 2019
The structural information of coronary arteries has important clinical value for quantitative diagnosis and treatment of coronary artery disease. In this study, a deep feature regression (DFR) method based on a convolutional regression network (CRN) and a stable point clustering mechanism for 3D vessel segmentation is proposed.
Jingliang Zhao, Danni Ai, Yang Yang, Hong Song, Yong Huang, Yongtian Wang, Jian Yang   +6 more
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Segmental phoneme recognition using piecewise linear regression

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002
We propose an efficient, self-organizing segmental measurement based on piecewise linear regression (PLR) fit of the short-term measurement trajectories. The advantages of this description are: (i) it serves to decouple temporal measurements from the recognition strategy; and, (ii) it leads to lesser computation as compared with conventional methods ...
S. Krishnan, P.V. Rao
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