Results 251 to 260 of about 357,766 (300)

Possibilistic Clustering Methods for Interval-Valued Data

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2014
Outliers may have many anomalous causes, for example, credit card fraud, cyberintrusion or breakdown of a system. Several research areas and application domains have investigated this problem. The popular fuzzy c-means algorithm is sensitive to noise and outlying data.
Almeida Pimentel, Bruno, De Souza, Renata M. C. R.   +1 more
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Three‐way component analysis of interval‐valued data

Journal of Chemometrics, 2004
AbstractVertices Principal Component Analysis (V‐PCA) and Centers Principal Component Analysis (C‐PCA) are variants of Principal Component Analysis (PCA) to deal with two‐way interval‐valued data. In this case the observation units are represented as hyperrectangles instead of points.
GIORDANI, Paolo, KIERS H. A. L.
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Resistant Regression for Interval-Valued Data

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, 2013
This paper introduces two new approaches to fit univariate resistant linear regression models on interval-valued data. Linear regressions on interval-valued data gives point predictions. The prediction of the lower and upper bounds from interval-valued data of dependent variable are estimated from the fitted range resistant linear regression model. The
Jobson Renan, Jornandes Dias Silva, Sergio Galdino   +2 more
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Interval-valued Data Analysis

International Journal of Business Analytics and Intelligence, 2015
N. A.
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The Mode of Interval-Valued Data

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019
When applied to interval-valued data, averaging aggregation functions do not, in general, produce a good representative value of an interval data set. For real-valued scalar and vector data the mode has been shown to be both a robust averaging function, insensitive to noise and outliers, and to produce a good representative value of a larger set.
Tim Wilkin, Gleb Beliakov
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Two-sample tests for interval-valued data

Journal of the Korean Statistical Society, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hyejeong Choi, Johan Lim, Donghyeon Yu, Minjung Kwak   +3 more
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Linear regression with interval‐valued data

WIREs Computational Statistics, 2015
Interval‐valued data refers to collection of observations in the form of intervals, rather than single numbers. It originally arose from situations of imprecision due to factors such as measurement or computation errors, where intervals are used to represent the true data points that are inside the intervals but not exactly known.
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Quantile regression of interval-valued data

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Linear regression is a standard statistical method widely used for prediction. It focuses on modeling the mean the target variable without accounting for all the distributional properties of this variable. In contrast, the quantile regression model facilitates the analysis of the full distributional properties, it allows to model different quantities ...
Roberta A.A. Fagundes, Renata M.C.R. de Souza, Yanne M.G. Soares   +2 more
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Constrained Robust Regression of Interval Valued Data

Journal of Statistical Theory and Practice
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
null Greeshmagiri, T. Palanisamy
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Regression Analysis for Interval-Valued Data

2000
When observations in large data sets are aggregated into smaller more manageable data sizes, the resulting classifications of observations invariably involve symbolic data. In this paper, covariance and correlation functions are introduced for interval-valued symbolic data.
L. Billard, E. Diday
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