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    Wyświetlanie 1-5 z 5
    Tytuł:
    Polygraph Tests - Benefits and Challenges
    Autorzy:
    Babu Rajan, Panthayil
    Powiązania:
    https://bibliotekanauki.pl/articles/1037954.pdf
    Data publikacji:
    2019
    Wydawca:
    Academicus. International Scientific Journal publishing house
    Tematy:
    polygraph
    autonomic responses
    truthfulness
    dishonesty
    misclassification
    Opis:
    This research describes the working, benefits and challengers of polygraph tests. Polygraph tests are lie-detecting devices that help ascertain individuals’ honesty based on physiological indicators. The heart rate/blood pressure, respiration, and skin responses are the three indicators measured in the test to assess honest/deceitful behavior. The underlying assumption behind the working of polygraph tests is that the autonomic responses of dishonest individuals are distinctively different from those of honest people because the liars will be more nervous than truth tellers. Control Question Test (CQT), Guilty Knowledge Test (GKT) or Concealed Information Test (CIT) and Neuroscience-Based Advanced Polygraph Tests are the important types of polygraph tests used today. Polygraph tests are used to detect truthfulness of individuals in such important fields as crime investigation departments, national security agencies, and business and industry. However, accuracy of polygraph tests and ethical issues associated with the tests are highly debated.
    Źródło:
    Academicus International Scientific Journal; 2019, 19; 146-155
    2079-3715
    2309-1088
    Pojawia się w:
    Academicus International Scientific Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new method for identifying outlying subsets of data
    Autorzy:
    Zalewska, M.
    Grzanka, A.
    Niemiro, W.
    Samoliński, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/970610.pdf
    Data publikacji:
    2008
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    misclassification error
    discriminant analysis
    multidimensional homogeneity test
    medical data
    Opis:
    In various branches of science, e.g. medicine, economics, sociology, it is necessary to identify or detect outlying subsets of data. Suppose that the set of data is partitioned into many relatively small subsets and we have some reason to suspect that one or several of these subsets may be atypical or aberrant. We propose applying a new measure of separability, based on the ideas borrowed from the discriminant analysis. In our paper we define two versions of this measure, both using a jacknife, leave-one-out, estimator of classification error. If a suspected subset is significantly well separated from the main bulk of data, then we regard it as outlying. The usefulness of our algorithm is illustrated on a set of medical data collected in a large survey "Epidemiology of Allergic Diseases in Poland" (ECAP). We also tested our method on artificial data sets and on the classical IRIS data set. For a comparison, we report the results of a homogeneity test of Bartoszyński, Pearl and Lawrence, applied to the same data sets.
    Źródło:
    Control and Cybernetics; 2008, 37, 3; 693-709
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Joint Calibration Estimator for dual frame surveys
    Autorzy:
    Elkasabi, Mahmoud A.
    Heeringa, Steven G.
    Lepkowski, James M.
    Powiązania:
    https://bibliotekanauki.pl/articles/465638.pdf
    Data publikacji:
    2015
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    dual-frame estimation
    calibration weighting
    auxiliary variables
    domain misclassification
    Opis:
    Many dual frame estimators have been proposed in the statistics literature. Some of these estimators are theoretically optimal but hard to apply in practice, whereas others are applicable but have larger variances than the first group. In this paper, a Joint Calibration Estimator (JCE) is proposed that is simple to apply in practice and meets many desirable properties for dual frame estimators. The JCE is asymptotically design unbiased conditional on the strong relationship between the estimation variable and the auxiliary variables employed in the calibration. The JCE achieves better performance when the auxiliary variables can fully explain the variability in the study variables or at least when the auxiliary variables are strong correlates of the estimation variables. As opposed to the standard dual frame estimators, the JCE does not require domain membership information. Even if included in the JCE auxiliary variables, the effect of the randomly misclassified domains does not exceed the random measurement error effect. Therefore, the JCE tends to be robust for the misclassified domains if included in the auxiliary variables. Meanwhile, the misclassified domains can significantly affect the unbiasedness of the standard dual frame estimators as proved theoretically and empirically in this paper.
    Źródło:
    Statistics in Transition new series; 2015, 16, 1; 7-36
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Validity test of the IPD-Work consortium approach for creating comparable job strain groups between Job Content Questionnaire and Demand-Control Questionnaire
    Autorzy:
    Choi, Bongkyoo
    Ko, Sangbaek
    Ostergren, Per-Olof
    Powiązania:
    https://bibliotekanauki.pl/articles/2177398.pdf
    Data publikacji:
    2015-03-26
    Wydawca:
    Instytut Medycyny Pracy im. prof. dra Jerzego Nofera w Łodzi
    Tematy:
    epidemiological studies
    Malmö
    sensitivity
    specificity
    scoring methods
    misclassification
    Opis:
    Objectives This study aims to test the validity of the IPD-Work Consortium approach for creating comparable job strain groups between the Job Content Questionnaire (JCQ) and the Demand-Control Questionnaire (DCQ). Material and Methods A random population sample (N = 682) of all middle-aged Malmö males and females was given a questionnaire with the 14-item JCQ and 11-item DCQ for the job control and job demands. The JCQ job control and job demands scores were calculated in 3 different ways: using the 14-item JCQ standard scale formulas (method 1); dropping 3 job control items and using the 11-item JCQ standard scale formulas with additional scale weights (method 2); and the approach of the IPD Group (method 3), dropping 3 job control items, but using the simple 11-item summation-based scale formulas. The high job strain was defined as a combination of high demands and low control. Results Between the 2 questionnaires, false negatives for the high job strain were much greater than false positives (37–49% vs. 7–13%). When the method 3 was applied, the sensitivity of the JCQ for the high job strain against the DCQ was lowest (0.51 vs. 0.60–0.63 when the methods 1 and 2 were applied), although the specificity was highest (0.93 vs. 0.87–0.89 when the methods 1 and 2 were applied). The prevalence of the high job strain with the JCQ (the method 3 was applied) was considerably lower (4–7%) than with the JCQ (the methods 1 and 2 were applied) and the DCQ. The number of congruent cases for the high job strain between the 2 questionnaires was smallest when the method 3 was applied. Conclusions The IPD-Work Consortium approach showed 2 major weaknesses to be used for epidemiological studies on the high job strain and health outcomes as compared to the standard JCQ methods: the greater misclassification of the high job strain and lower prevalence of the high job strain.
    Źródło:
    International Journal of Occupational Medicine and Environmental Health; 2015, 28, 2; 321-333
    1232-1087
    1896-494X
    Pojawia się w:
    International Journal of Occupational Medicine and Environmental Health
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of a Function of Misclassified Binary Data
    Autorzy:
    Al-Kandari, Noriah M.
    Powiązania:
    https://bibliotekanauki.pl/articles/973541.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    binary classification
    double sampling
    finite population sampling
    misclassification
    linkage error
    sampling design
    Opis:
    We consider the problem of predicting a function of misclassified binary variables. We make an interesting observation that the naive predictor, which ignores the misclassification errors, is unbiased even if the total misclassification error is high as long as the probabilities of false positives and false negatives are identical. Other than this case, the bias of the naive predictor depends on the misclassification distribution and the magnitude of the bias can be high in certain cases. We correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a biascorrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE).
    Źródło:
    Statistics in Transition new series; 2016, 17, 3; 429-448
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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