Results 61 to 70 of about 526,550 (191)
Detecting and handling outlying trajectories in irregularly sampled functional datasets [PDF]
, 2009 Outlying curves often occur in functional or longitudinal datasets, and can be very influential on parameter estimators and very hard to detect visually.
Gervini, Daniel
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Outlier sums for differential gene expression analysis [PDF]
Biostatistics, 2006 We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples.
Robert, Tibshirani, Trevor, Hastie
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Outlier robust cointegration analysis [PDF]
, 1995 Standard unit root tests and cointegration tests are sensitive to atypical events such as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we obtain weights for all observations in the sample.
Franses, Philip Hans, Lucas, André
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An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset
IEEE Access, 2018 Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers.
Hui Lu +3 more
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GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. [PDF]
PLoS ONE, 2011 BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given ...
John Patrick Mpindi +13 more
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RNA-Seq optimization with eQTL gold standards. [PDF]
, 2013 BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results ...
Arking, Dan E +5 more
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New models for describing outliers in meta‐analysis [PDF]
Research Synthesis Methods, 2015 An unobserved random effect is often used to describe the between‐study variation that is apparent in meta‐analysis datasets. A normally distributed random effect is conventionally used for this purpose. When outliers or other unusual estimates are included in the analysis, the use of alternative random effect distributions has previously been proposed.
Baker, RD, Jackson, D
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CLASSIFICATION OF TASKS OF DATA MINING AND DATA PROCESSING IN THE ECONOMY
Baltic Journal of Economic Studies, 2017 The subject of the paper is methods of classification of data mining and data processing tasks in the economy, as well as their classification characteristics. Methodology.
Aleksey Mints
doaj
Robust group analysis using outlier inference
NeuroImage, 2008 Neuroimaging group studies are typically performed with the assumption that subjects used are randomly drawn from a population of subjects. The population of subjects is assumed to have a distribution of effect sizes associated with it that are Gaussian distributed.
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International Journal of Distributed Sensor Networks, 2015 Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge.
Haiqing Yao, Heng Cao, Jin Li
doaj +1 more source