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An outliers detection and elimination framework in classification task of data mining

Dash, Ch. Sanjeev Kumar; Behera, Ajit Kumar; Dehuri, … - In: Decision analytics journal 6 (2023), pp. 1-8

An outlier is a datum that is far from other data points in which it occurs. It can have a considerable impact on the output. Therefore, removing or resolving it before the analysis is essential to prevent skewing. Outliers in a survey sampling can have a significant outcome on statistical...

Persistent link: https://www.econbiz.de/10014505859

A Simple Diagnostic Plot Connecting Robust Estimation, Outlier Detection, and False Discovery Rates

Rice, Kenneth; Spiegelhalter, David - In: Journal of Applied Statistics 33 (2006) 10, pp. 1131-1147

Robust estimation of parameters, and identification of specific data points that are discordant with an assumed model, are often treated as different statistical problems. The two aims are, however, closely inter-related and in many cases the two analyses are required simultaneously. We present...

Persistent link: https://www.econbiz.de/10009225460

Estimation of Commodity Specific Production Costs Using German Farm Accountancy Data

Bahta, Sirak Teclemariam; Berner, Anja; Offermann, Frank - European Association of Agricultural Economists - EAAE - 2011

A central problem in estimating per unit costs of production originates from the fact that most farms produce multiple outputs and standard farm-accounting data are only available at the whole-farm level. The seemingly unrelated regression (SUR) approach is used to estimate per unit production...

Persistent link: https://www.econbiz.de/10009326141

Robust clustering around regression lines with high density regions

Cerioli, Andrea; Perrotta, Domenico - In: Advances in Data Analysis and Classification 8 (2014) 1, pp. 5-26

Robust methods are needed to fit regression lines when outliers are present. In a clustering framework, outliers can be … operation on a point pattern generated by different components. We then apply robust clustering methods to the thinned data set …

Persistent link: https://www.econbiz.de/10010758714

Semi-supervised outlier detection based on fuzzy rough C-means clustering

Xue, Zhenxia; Shang, Youlin; Feng, Aifen - In: Mathematics and Computers in Simulation (MATCOM) 80 (2010) 9, pp. 1911-1921

samples and fuzzy rough C-means clustering. This method introduces an objective function, which minimizes the sum squared … error of clustering results and the deviation from known labeled examples as well as the number of outliers. Each cluster is … represented by a center, a crisp lower approximation and a fuzzy boundary by using fuzzy rough C-means clustering and only those …

Persistent link: https://www.econbiz.de/10010870145

Robust estimation of the parameters of g-and-h distributions, with applications to outlier detection

Xu, Yihuan; Iglewicz, Boris; Chervoneva, Inna - In: Computational Statistics & Data Analysis 75 (2014) C, pp. 66-80

The g-and-h distributional family is generated from a relatively simple transformation of the standard normal and can approximate a broad spectrum of distributions. Consequently, it is easy to use in simulation studies and has been applied in multiple areas, including risk management, stock...

Persistent link: https://www.econbiz.de/10010871306

Benchmark testing of algorithms for very robust regression: FS, LMS and LTS

Torti, Francesca; Perrotta, Domenico; Atkinson, Anthony C. - In: Computational Statistics & Data Analysis 56 (2012) 8, pp. 2501-2512

The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust regression rely on selecting numerous subsamples of the data. New algorithms for LMS and LTS estimators that have increased computational efficiency due to improved combinatorial sampling are...

Persistent link: https://www.econbiz.de/10010871350

Detecting money laundering using filtering techniques: a multiple-criteria index

Yang, Shenggang; Wei, Lai - In: Journal of Economic Policy Reform 13 (2010) 2, pp. 159-178

Money laundering is a dynamic activity attempting to circumvent anti-money laundering (AML) actions. We propose a money-laundering detection approach encompassing three separate detection measures applied simultaneously, providing a consolidated index to minimize circumvention. The index...

Persistent link: https://www.econbiz.de/10009208083

Mathematical programming for simultaneous feature selection and outlier detection under l1 norm

Barbato, Michele; Ceselli, Alberto - In: European journal of operational research : EJOR 316 (2024) 3, pp. 1070-1084

Persistent link: https://www.econbiz.de/10014577855

A systematic approach for data validation using data-driven visualisations and interactive reporting

Zaccardi, Jules; Infante, Enrico - In: Eurostat review on national accounts and macroeconomic … (2021), pp. 67-89

In this article we present the development of a tool that can serve most official statistics. This tool is fully interactive and based mostly on visualisations, which makes it a powerful ally to validate or analyse data. To make our tool a pragmatic solution in an era facing ever larger volumes...

Persistent link: https://www.econbiz.de/10013347483