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    Tytuł:
    Review of methods for data sets with missing values and practical applications
    Autorzy:
    Korczyński, Adam
    Powiązania:
    https://bibliotekanauki.pl/articles/433946.pdf
    Data publikacji:
    2014
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    missing data pattern
    missing data mechanism
    complete-case analysis
    available-case analysis
    single imputation
    likelihood-based methods
    multiple imputation
    weighting methods
    Opis:
    The aim of this paper is to revise the traditional methods (complete-case analysis, available-case analysis, single imputation) and current methods (likelihood-based methods, multiple imputation, weighting methods) for handling the problem of missing data and to assess their usefulness in statistical research. The paper provides the terminology and the description of traditional and current methods and algorithms used in the analysis of incomplete data sets. The methods are assessed in terms of the statistical properties of their estimators. An example is provided for the multiple imputation method. The review indicates that current methods outweigh traditional ones in terms of bias reduction, precision and efficiency of the estimation.
    Źródło:
    Śląski Przegląd Statystyczny; 2014, 12(18); 83-104
    1644-6739
    Pojawia się w:
    Śląski Przegląd Statystyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Predicting in multivariate incomplete time series. Application of the expectation-maximisation algorithm supplemented by the Newton-Raphson method
    Autorzy:
    Korczyński, Adam
    Powiązania:
    https://bibliotekanauki.pl/articles/1806793.pdf
    Data publikacji:
    2021-08-24
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    missing data
    multivariate time series
    expectation-maximisation algorithm
    Newton-Raphson algorithm
    Opis:
    Statistical practice requires various imperfections resulting from the nature of data to be addressed. Data containing different types of measurement errors and irregularities, such as missing observations, have to be modelled. The study presented in the paper concerns the application of the expectation-maximisation (EM) algorithm to calculate maximum likelihood estimates, using an autoregressive model as an example. The model allows describing a process observed only through measurements with certain level of precision and through more than one data series. The studied series are affected by a measurement error and interrupted in some time periods, which causes the information for parameters estimation and later for prediction to be less precise. The presented technique aims to compensate for missing data in time series. The missing data appear in the form of breaks in the source of the signal. The adjustment has been performed by the EM algorithm to a hybrid version, supplemented by the Newton-Raphson method. This technique allows the estimation of more complex models. The formulation of the substantive model of an autoregressive process affected by noise is outlined, as well as the adjustment introduced to overcome the issue of missing data. The extended version of the algorithm has been verified using sampled data from a model serving as an example for the examined process. The verification demonstrated that the joint EM and Newton-Raphson algorithms converged with a relatively small number of iterations and resulted in the restoration of the information lost due to missing data, providing more accurate predictions than the original algorithm. The study also features an example of the application of the supplemented algorithm to some empirical data (in the calculation of a forecasted demand for newspapers).
    Źródło:
    Przegląd Statystyczny; 2021, 68, 1; 17-46
    0033-2372
    Pojawia się w:
    Przegląd Statystyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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