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  • Statistics In Transition


Article | 20-September-2020

Detection of Outliers in Univariate Circular Data by Means of the Outlier Local Factor (LOF)

The problem of outlier detection in univariate circular data was the object of increased interest over the last decade. New numerical and graphical methods were developed for samples from different circular probability distributions. The main drawback of the existing methods is, however, that they are distribution-based and ignore the problem of multiple outliers. The local outlier factor (LOF) is a density-based method for detecting outliers in multivariate data and it depends on the local

Ali H. Abuzaid

Statistics in Transition New Series, Volume 21 , ISSUE 3, 39–51

Article | 06-July-2017


This article considers a robust hierarchical Bayesian approach to deal with random effects of small area means when some of these effects assume extreme values, resulting in outliers. In the presence of outliers, the standard Fay-Herriot model, used for modeling area-level data, under normality assumptions of random effects may overestimate the random effects variance, thus providing less than ideal shrinkage towards the synthetic regression predictions and inhibiting the borrowing of

Adrijo Chakraborty, Gauri Sankar Datta, Abhyuday Mandal

Statistics in Transition New Series, Volume 17 , ISSUE 1, 67–90

Research paper | 01-November-2017


Skewed distributions with representative outliers pose a problem in many surveys. Various small area prediction approaches for skewed data based on transformation models have been proposed. However, in certain applications of those predictors, the fact that the survey data also contain a non-negligible number of zero-valued observations is sometimes dealt with rather crudely, for instance by arbitrarily adding a constant to each value (to allow zeroes to be considered as “positive observations

Forough Karlberg

Statistics in Transition New Series, Volume 16 , ISSUE 4, 541–562

Article | 07-July-2017


Recent years have seen a dynamic development in statistical methods for analysing data contaminated with outliers. One of the more important techniques that can deal with outlying observations is robust regression, which represents four decades of research. Until recently the implementation of robust regression methods, such as M-estimation or MM-estimation, was limited owing to their iterative nature. With advances in computing power and the growing availability of statistical packages, such

Grażyna Dehnel

Statistics in Transition New Series, Volume 17 , ISSUE 4, 749–762



Machine learning methods are increasingly being used to predict company bankruptcy. Comparative studies carried out on selected methods to determine their suitability for predicting company bankruptcy have demonstrated high levels of prediction accuracy for the extreme gradient boosting method in this area. This method is resistant to outliers and relieves the researcher from the burden of having to provide missing data. The aim of this study is to assess how the elimination of outliers from

Barbara Pawełek

Statistics in Transition New Series, Volume 20 , ISSUE 2, 155–171

Research paper | 31-October-2017


There are many sample surveys of populations that contain outliers (extreme values). This is especially true in business, agricultural, household and medicine surveys. Outliers can have a large distorting influence on classical statistical methods that are optimal under the assumption of normality or linearity. As a result, the presence of extreme observations may adversely affect estimation, especially when it is carried out at a low level of aggregation. To deal with this problem, several

Grażyna Dehnel

Statistics in Transition New Series, Volume 16 , ISSUE 1, 137–152

Research Article | 20-February-2013


Outlier detection plays a crucial role in secure monitoring in Wireless Sensor Networks (WSN). Moreover, outlier detection techniques in WSN face the problem of limited resources of transmission bandwidth, energy consumption and storage capacity. In this paper, similar flocking model is proposed and a cluster algorithm based on similar flocking model (CASFM) is put forward to detect outliers in real-time stream data collected by sensor nodes. The similar flocking model improves the Vicsek model

Cheng Chunling, Wu Hao, Yu Zhihu, Zhang Dengyin, Xu Xiaolong

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 18–37

Article | 15-September-2020

High dimensional, robust, unsupervised record linkage

We develop a technique for record linkage on high dimensional data, where the two datasets may not have any common variable, and there may be no training set available. Our methodology is based on sparse, high dimensional principal components. Since large and high dimensional datasets are often prone to outliers and aberrant observations, we propose a technique for estimating robust, high dimensional principal components. We present theoretical results validating the robust, high dimensional

Sabyasachi Bera, Snigdhansu Chatterjee

Statistics in Transition New Series, Volume 21 , ISSUE 4, 123–143

Article | 20-September-2020

A Bayesian Small Area Model with Dirichlet Processes on the Responses

Typically survey data have responses with gaps, outliers and ties, and the distributions of the responses might be skewed. Usually, in small area estimation, predictive inference is done using a two-stage Bayesian model with normality at both levels (responses and area means). This is the Scott-Smith (S-S) model and it may not be robust against these features. Another model that can be used to provide a more robust structure is the two-stage Dirichlet process mixture (DPM) model, which has

Jiani Yin, Balgobin Nandram

Statistics in Transition New Series, Volume 21 , ISSUE 3, 1–19

Research Article | 13-June-2021

Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling

Ahmed Audu, Rajesh Singh, Supriya Khare

Statistics in Transition New Series, Volume 22 , ISSUE 2, 125–142

Article | 01-March-2015


Visual tracking algorithm based on binary classification has become the research hot issue. The tracking algorithm firstly constructs a binary classifier between object and background, then to determine the object’s location by the probability of the classifier. However, such binary classification may not fully handle the outliers, which may cause drifting. To improve the robustness of these tracking methods, a novel object tracking algorithm is proposed based on support vector machine (SVM

Gao Xiaoxing, Liu Feng

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 255–271

Article | 15-September-2020

Skew normal small area time models for the Brazilian annual service sector survey

produce estimates with acceptable precision for service activities in the North, Northeast and Midwest regions of the country. Therefore, the use of small area estimation models may provide acceptable precise estimates, especially if they take into account temporal dynamics and sector similarity. Besides, skew normal models can handle business data with asymmetric distribution and the presence of outliers. We propose models with domain and time random effects on the intercept and slope. The results

André Felipe Azevedo Neves, Denise Britz do Nascimento Silva, Fernando Antônio da Silva Moura

Statistics in Transition New Series, Volume 21 , ISSUE 4, 84–102

Article | 18-March-2020

Robust estimation of wages in small enterprises:  the application to Poland’s districts

The paper presents an empirical study designed to test a small area estimation method. The aim of the study is to apply a robust version of the Fay-Herriot model to the estimation of average wages in the small business sector. Unlike the classical Fay-Herriot model, its robust version makes it possible to meet the assumption of normality of random effects under the presence of outliers. Moreover, the use of this version of the Fay-Herriot model helps to improve the precision of estimates

Grażyna Dehnel, Łukasz Wawrowski

Statistics in Transition New Series, Volume 21 , ISSUE 1, 137–157

Article | 01-June-2020

The positional MEF-TOPSIS method for the assessment of complex economic phenomena in territorial units

In this paper, the authors propose a new methodological approach to the construction of a synthetic measure, where the objects are described by variables with strong asymmetry and extreme values (outliers). Even a single extreme value (very large or very small) of a variable for the object may significantly affect the attribution of an excessively high or low rank in the final ranking of objects. This dependence is particularly apparent when using the classical TOPSIS (Technique for Order of

Aleksandra Łuczak, Małgorzata Just

Statistics in Transition New Series, Volume 21 , ISSUE 2, 157–172

Research paper | 31-October-2017


in values of important economic indicators. Growth rates are standardized by dividing values of original change rates by medians specified based on spatio- temporal data modules. Such division results in each characteristic being brought to equal validity. Simultaneously, the original character is maintained and variables are not “flattened” by the outliers. Changing destimulants into stimulants occurs during growth rates calculation. The measure of resilience to crisis is calculated as an

Małgorzata Markowska

Statistics in Transition New Series, Volume 16 , ISSUE 2, 293–308

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