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    Wyświetlanie 1-3 z 3
    Tytuł:
    The effect of binary data transformation in categorical data clustering
    Autorzy:
    Cibulková, Jana
    Šulc, Zdenek
    Sirota, Sergej
    Rezanková, Hana
    Powiązania:
    https://bibliotekanauki.pl/articles/1194463.pdf
    Data publikacji:
    2019-07-02
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    hierarchical cluster analysis
    nominal variable
    binary variable
    categorical data
    similarity measures
    evaluation criteria
    generated data
    Opis:
    This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of information in the data or decrease the speed of the computations. The second approach uses similarity measures developed for the categorical data. But these measures are not so well examined as the binary ones and they are not implemented in commercial software. The comparison of these two approaches is performed on generated data sets with categorical variables and the evaluation is done using both the internal and the external evaluation criteria. The purpose of this paper is to show that the binary transformation is not necessary in the process of clustering categorical data since the second approach leads to at least comparably good clustering results as the first approach.
    Źródło:
    Statistics in Transition new series; 2019, 20, 2; 33-47
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Methods for combining probability and nonprobability samples under unknown overlaps
    Autorzy:
    Savitsky, Terrance D.
    Williams, Matthew R.
    Gershunskaya, Julie
    Beresovsky, Vladislav
    Powiązania:
    https://bibliotekanauki.pl/articles/31342142.pdf
    Data publikacji:
    2023-12-07
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Survey sampling
    Nonprobability sampling
    Data combining
    Inclusion probabilities
    Exact sample likelihood
    Bayesian hierarchical modeling
    Opis:
    Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias since the distribution of variables of interest in the convenience sample is different from the population distribution. A recent set of approaches estimates inclusion probabilities for convenience sample units by specifying reference sample-weighted pseudo likelihoods. This paper introduces a novel approach that derives the propensity score for the observed sample as a function of inclusion probabilities for the reference and convenience samples as our main result. Our approach allows specification of a likelihood directly for the observed sample as opposed to the approximate or pseudo likelihood. We construct a Bayesian hierarchical formulation that simultaneously estimates sample propensity scores and the convenience sample inclusion probabilities. We use a Monte Carlo simulation study to compare our likelihood based results with the pseudo likelihood based approaches considered in the literature.
    Źródło:
    Statistics in Transition new series; 2023, 24, 5; 1-34
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Development of Small Area Estimationin Official Statistics
    Autorzy:
    Kordos, Jan
    Powiązania:
    https://bibliotekanauki.pl/articles/466085.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    small area estimation
    official statistics
    sampling survey
    direct estimation
    indirect estimation
    empirical Bayes estimator
    hierarchical Bayes estimator
    data quality
    Opis:
    The author begins with a general assessment of the mission of the National Statistics Institutes (NSIs), main producers of official statistics, which are obliged to deliver high quality statistical information on the state and evolution of the population, the economy, the society and the environment. These statistical results must be based on scientific principles and methods. They must be made available to the public, politics, economy and research for decision-making and information purposes. Next, before discussing general issues of small area estimation (SAE) in official statistics, the author reminds: the methods of sampling surveys, data collection, estimation procedures, and data quality assessment used for official statistics. Statistical information is published in different breakdowns with stable or even decreasing budget while being legally bound to control the response burden. Special attention is paid, from a practitioner point of view, to synthetic development of small area estimation in official statistics, beginning with international seminars and conferences devoted to SAE procedures and methods (starting with the Canadian symposium, 1985, and the Warsaw conference, 1992, to the Poznan conference, Poland, 2014), and some international projects (EURAREA, SAMPLE, BIAS, AMELI, ESSnet). Next, some aspects of development of SAE in official statistics are discussed. At the end some conclusions regarding quality of SAE procedures are considered.
    Źródło:
    Statistics in Transition new series; 2016, 17, 1; 105-132
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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