Results 221 to 230 of about 190,177 (262)

Outlier detection in data envelopment analysis: an analysis of jackknifing

Journal of the Operational Research Society, 2002
Summary: This paper analyzes the resampling technique of jackknifing and its capability of detecting outliers in data envelopment analysis. It is well recognized that measured efficiency is sensitive to outliers; recent research has employed resampling techniques to estimate standard deviations in an attempt to handle outliers.
Jan Ondrich, John Ruggiero
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Bayesian analysis for outliers in survey sampling

Computational Statistics & Data Analysis, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Purchasing Pattern of the Customers Based On Outliers Analysis

SSRN Electronic Journal, 2019
Data Mining (referred as extracting knowledge from data) is the process of discovering patterns, associations, and links in the huge stack of data which is based on the analysis done through different perspectives. There are many disciplines which are found under data mining some of them are clustering analysis, regression analysis, and classification ...
Rakhee Chhibber, Chetan Chadha
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Mobile Surveillance by 3D-Outlier Analysis

2011
We present a novel online method to model independent foreground motion by using solely traditional structure and motion (S+M) algorithms. On the one hand, the visible static scene can be reconstructed and on the other hand, the position and orientation (pose) of the observer (mobile camera) are estimated.
Peter Holzer, Axel Pinz
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An Introduction to Outlier Analysis

2012
Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data mining and statistics literature. In most applications, the data is created by one or more generating processes, which could either reflect activity in the system or observations collected about entities.
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Applications of Outlier Analysis

2012
Outlier analysis has numerous applications in a wide variety of domains, such as the financial industry, quality control, fault diagnosis, intrusion detection, Web analytics, and medical diagnosis. The applications of outlier analysis are so diverse that it is impossible to exhaustively cover all possibilities in a single chapter.
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Correspondence Analysis in the Case of Outliers

2012
Analysis of categorical data by means of Correspondence Analysis (CA) has recently become popular. The behavior of CA in the presence of outliers in the table is not sufficiently explored in the literature, especially in the case of multidimensional contingency tables.
Anna Langovaya, Sonja Kuhnt, Hamdi Chouikha   +2 more
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Isotopic Ratio Outlier Analysis (IROA) for Quantitative Analysis

2019
There is always a tension within the omics sciences between trying to measure biological molecules rapidly and measuring accurately. Metabolomics as an omics science tries to measure the small biochemicals rapidly, in a single pass, but the current state of the art cannot provide the reproducibility or accuracy needed for clinical use or even daily ...
Chris, Beecher, Felice A, de Jong
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Cluster Analysis for Outlier Detection

2009
For many applications in knowledge discovery in databases finding outliers, rare events, is of importance. Outliers are observations, which deviate significantly from the rest of the data, so that it seems they are generated by another process (Hawkins, 1980).
Frank Klawonn, Frank Rehm
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A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Big Data and Cognitive Computing, 2021
Raed Alsini, Omar Alghushairy, Xiaogang Ma   +2 more
exaly