Results 31 to 40 of about 526,550 (191)
Alphanumeric Journal, 2015 Yasam cozumlemesi, tanimlanan herhangi bir olayin ortaya cikmasina kadar gecen surenin incelenmesinde kullanilan istatistiksel yontemler butunudur. Yasam cozumlemesinde aykiri degerler klasik regresyonda kullanilan yontemlerden farkli yontemler kullanilarak hesaplanmaktadir. Yasam cozumlemesinde aykiri deger belirleme yontemleri artiklara ve artiklarin
KARASOY, Durdu, TUNCER, Nuray
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Outliers in data envelopment analysis [PDF]
Journal of Centrum Cathedra, 2016 Purpose The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error. Design/methodology/approach The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier.
Taylor Boyd, Grace Docken, John Ruggiero
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Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization [PDF]
, 2011 Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics.
Giannakis, Georgios B., Mateos, Gonzalo
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Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots
Cancer Informatics, 2006 We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays.
Song Yang +8 more
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Genetic Variation and Breeding Signature in Mass Selection Lines of the Pacific Oyster (Crassostrea gigas) Assessed by SNP Markers. [PDF]
PLoS ONE, 2016 In breeding industries, a challenging problem is how to keep genetic diversity over generations. To investigate genetic variation and identify breeding signatures in mass selected lines of Pacific oyster (Crassostrea gigas), three sixth-generation ...
Xiaoxiao Zhong +4 more
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Randomized Robust Subspace Recovery for High Dimensional Data Matrices
, 2016 This paper explores and analyzes two randomized designs for robust Principal Component Analysis (PCA) employing low-dimensional data sketching. In one design, a data sketch is constructed using random column sampling followed by low dimensional embedding,
Atia, George, Rahmani, Mostafa
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Outlier Analysis of Categorical Data using NAVF [PDF]
Informatică economică, 2013 Outlier mining is an important task to discover the data records which have an exceptional behavior comparing with other records in the remaining dataset. Outliers do not follow with other data objects in the dataset.
D. LAKSHMI SREENIVASA REDDY +2 more
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Outlier Detection for Welfare Analysis
, 2022 Extreme values are common in survey data and represent a recurring threat to the reliability of both poverty and inequality estimates. The adoption of a consistent criterion for outlier detection is useful in many practical applications, particularly when international and intertemporal ...
Belotti, Federico +2 more
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Early identification of scientific breakthroughs through outlier analysis based on research entities
Journal of Data and Information ScienceTo address the “anomalies” that occur when scientific breakthroughs emerge, this study focuses on identifying early signs and nascent stages of breakthrough innovations from the perspective of outliers, aiming to achieve early identification of ...
Zhao Yang +3 more
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An Incremental Local Outlier Detection Method in the Data Stream
Applied Sciences, 2018 Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging.
Haiqing Yao +3 more
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