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    Wyświetlanie 1-4 z 4
    Tytuł:
    Robust regression in monthly business survey
    Autorzy:
    Dehnel, Grażyna
    Powiązania:
    https://bibliotekanauki.pl/articles/466100.pdf
    Data publikacji:
    2015
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    robust regression
    outlier detection
    business statistics
    Opis:
    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 alternative techniques of estimation, less sensitive to outliers, have been proposed in the statistical literature. In this paper we attempt to apply and assess some robust regression methods (LTS, M-estimation, S-estimation, MM-estimation) in the business survey conducted within the framework of official statistics.
    Źródło:
    Statistics in Transition new series; 2015, 16, 1; 137-152
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extreme gradient boosting method in the prediction of company bankruptcy
    Autorzy:
    Pawełek, Barbara
    Powiązania:
    https://bibliotekanauki.pl/articles/1194455.pdf
    Data publikacji:
    2019-07-02
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    XGBoost
    company bankruptcy
    machine learning
    outlier
    Opis:
    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 data sets affects the accuracy of the extreme gradient boosting method in predicting company bankruptcy. The added value of this study is demonstrated by the application of the extreme gradient boosting method in bankruptcy prediction based on data free from the outliers reported for companies which continue to operate as a going concern. The research was conducted using 64 financial ratios for the companies operating in the industrial processing sector in Poland. The research results indicate that it is possible to increase the detection rate for bankrupt companies by eliminating the outliers reported for companies which continue to operate as a going concern from data sets.
    Źródło:
    Statistics in Transition new series; 2019, 20, 2; 155-171
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Outlier detection based on the functional coefficient of variation
    Autorzy:
    Deveci Kocakoç, Ipek
    Köymen Keser, Istem
    Powiązania:
    https://bibliotekanauki.pl/articles/12289238.pdf
    Data publikacji:
    2023-03-15
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    coefficient of variation function
    outlier detection
    functional data analysis
    Opis:
    The coefficient of the variation function is a useful descriptive statistic, especially when comparing the variability of more than two curve groups, even when they have significantly different mean curves. Since the coefficient of variation function is the ratio of the mean and standard deviation functions, its particular property is that it shows the acceleration more explicitly than the standard deviation function. The aim of the study is twofold: to show that the functional coefficient of variation is more sensitive to abrupt changes than the functional standard deviation and to propose the utilisation of the functional coefficient of variation as an outlier detection tool. Several simulation trials have shown that the coefficient of the variation function allows the effects of outliers to be seen explicitly.
    Źródło:
    Statistics in Transition new series; 2023, 24, 2; 1-16
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Outlier detection in the analysis of nested Gage R&R, random effect model
    Autorzy:
    Abduljaleel, Mohammed
    Midi, Habshah
    Karimi, Mostafa
    Powiązania:
    https://bibliotekanauki.pl/articles/1192468.pdf
    Data publikacji:
    2019-08-30
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    measurement system analysis
    mm location
    nested Gage R&R
    outlier
    residuals
    Opis:
    Measurement system analysis is a comprehensive valuation of a measurement process and characteristically includes a specially designed experiment that strives to isolate the components of variation in that measurement process. Gage repeatability and reproducibility is the adequate technique to evaluate variations within the measurement system. Repeatability refers to the measurement variation obtained when one person repeatedly measures the same item with the same Gage, while reproducibility refers to the variation due to different operators using the same Gage. The two factors factorial design, either crossed or nested factor, is usually used for a Gage R&R study. In this study, the focus is only on the nested factor, random effect model. Presently, the classical method (the method of analysing data without taking into consideration the existence of outliers) is used to analyse the nested Gage R&R data. However, this method is easily affected by outliers and, consequently, the measurement system’s capability is also affected. Therefore, the aims of this study are to develop an identification method to detect outliers and to formulate a robust method of measurement analysis of nested Gage R&R, random effect model. The proposed methods of outlier detection are based on a robust mm location and scale estimators of the residuals. The results of the simulation study and real numerical example show that the proposed outlier identification method and the robust estimation method are the most successful methods for the detection of outliers.
    Źródło:
    Statistics in Transition new series; 2019, 20, 3; 31-56
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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