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    Wyświetlanie 1-5 z 5
    Tytuł:
    Detection of Outliers in Univariate Circular Data by Means of the Outlier Local Factor (LOF)
    Autorzy:
    Abuzaid, Ali H.
    Powiązania:
    https://bibliotekanauki.pl/articles/1058939.pdf
    Data publikacji:
    2020-09-04
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    discordancy
    distance
    multiple outliers
    neighbours
    spacing theory
    Opis:
    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 density of every k nearest neighbours. The aim of this paper is to extend the application of the LOF to the detection of possible outliers in circular samples, where the angles of circular data are represented in two Cartesian coordinates and treated as bivariate data. The performance of the LOF is compared against other existing numerical methods by means of a simulation based on the power of a test and the proportion of correct detection. The LOF performance is compatible with the best existing discordancy tests, while outperforming other tests. The level of the LOF performance is directly related to the contamination and concentration parameters, while having an inverse relationship with the sample size. In order to illustrate the process, the LOF and other existing discordancy tests are applied to detect possible outliers in two common real circular datasets.
    Źródło:
    Statistics in Transition new series; 2020, 21, 3; 39-51
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling
    Autorzy:
    Audu, Ahmed
    Singh, Rajesh
    Khare, Supriya
    Powiązania:
    https://bibliotekanauki.pl/articles/1054567.pdf
    Data publikacji:
    2021-06-04
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    calibration
    outliers
    percentage relative efficiency (PRE)
    stratified sampling
    Opis:
    In this paper, two modified, design-based calibration ratio-type estimators are presented. The suggested estimators were developed under stratified random sampling using information on an auxiliary variable in the form of robust statistical measures, including Gini’s mean difference, Downton’s method and probability weighted moments. The properties (biases and MSEs) of the proposed estimators are studied up to the terms of firstorder approximation by means of Taylor’s Series approximation. The theoretical results were supported by a simulation study conducted on four bivariate populations and generated using normal, chi-square, exponential and gamma populations. The results of the study indicate that the proposed calibration scheme is more precise than any of the others considered in this paper.
    Źródło:
    Statistics in Transition new series; 2021, 22, 2; 125-142
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Small Area Estimation for Skewed Data in the Presence of Zeroes
    Autorzy:
    Karlberg, Forough
    Powiązania:
    https://bibliotekanauki.pl/articles/466079.pdf
    Data publikacji:
    2015
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    small area estimation
    representative outliers
    zero-valued observations lognormal-logistic mixture model
    Opis:
    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, only smaller”, instead of acknowledging their qualitatively different nature). On the other hand, while a lognormal-logistic model has been proposed (to incorporate skewed distributions as well as zeroes), that model does not include any hierarchical aspects, and is therefore not explicitly adapted to small area prediction. In this paper, we consolidate the two approaches by extending one of the already established log-transformation mixed small area prediction models to incorporate a logistic component. This allows for the simultaneous, systematic treatment of domain effects, outliers and zero-valued observations in a single framework. We benchmark the resulting model-based predictors (against relevant alternatives) in applications to simulated data as well as empirical data from the Australian Agricultural and Grazing Industries Survey.
    Źródło:
    Statistics in Transition new series; 2015, 16, 4; 541-562
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Two-Component Normal Mixture Alternative to the Fay-Herriot Model
    Autorzy:
    Chakraborty, Adrijo
    Datta, Gauri Sankar
    Mandal, Abhyuday
    Powiązania:
    https://bibliotekanauki.pl/articles/465632.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Hierarchical Bayes
    heavy-tail distribution
    non-informative priors
    robustness to outliers
    small area estimation
    Opis:
    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 information. Even a small number of substantive outliers of random effects results in a large estimate of the random effects variance in the Fay-Herriot model, thereby achieving little shrinkage to the synthetic part of the model or little reduction in the posterior variance associated with the regular Bayes estimator for any of the small areas. While the scale mixture of normal distributions with a known mixing distribution for the random effects has been found to be effective in the presence of outliers, the solution depends on the mixing distribution. As a possible alternative solution to the problem, a two-component normal mixture model has been proposed, based on non-informative priors on the model variance parameters, regression coefficients and the mixing probability. Data analysis and simulation studies based on real, simulated and synthetic data show an advantage of the proposed method over the standard Bayesian Fay-Herriot solution derived under normality of random effects.
    Źródło:
    Statistics in Transition new series; 2016, 17, 1; 67-90
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Interval shrinkage estimation of the parameter of exponential distribution in the presence of outliers under loss functions
    Autorzy:
    Nasiri, Parviz
    Powiązania:
    https://bibliotekanauki.pl/articles/2108120.pdf
    Data publikacji:
    2022-09-14
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    interval information
    mean square error
    shrinkage estimator
    exponential distribution
    uniform distribution
    outliers
    Linex loss function
    Opis:
    In this paper, we studied estimators based on an interval shrinkage with equal weights point shrinkage estimators for all individual target points θ¯ ∈ (θ0, θ1) for exponentially distributed observations in the presence of outliers drawn from a uniform distribution. Estimators obtained from both shrinkage and interval shrinkage were compared, showing that the estimators obtained via the interval shrinkage method perform better. Symmetric and asymmetric loss functions were also used to calculate the estimators. Finally, a numerical study and illustrative examples were provided to describe the results.
    Źródło:
    Statistics in Transition new series; 2022, 23, 3; 65-78
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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