- Autor
- Pettersson Nicklas (Stockholm University, Sweden)
- Tytuł
- Bias Reduction of Finite Population Imputation by Kernel Methods
- Źródło
- Statistics in Transition, 2013, vol. 14, nr 1, s. 139-160, aneks, tab., bibliogr. 36 poz.
- Słowa kluczowe
- Metody samowsporne, Estymacja, Statystyka matematyczna, Analiza statystyczna, Symulacja
Bootstrap, Estimation, Mathematical statistics, Statistical analysis, Simulation - Uwagi
- summ.
- Abstrakt
- Missing data is a nuisance in statistics. Real donor imputation can be used with item nonresponse. A pool of donor units with similar values on auxiliary variables is matched to each unit with missing values. The missing value is then replaced by a copy of the corresponding observed value from a randomly drawn donor. Such methods can to some extent protect against nonresponse bias. But bias also depends on the estimator and the nature of the data. We adopt techniques from kernel estimation to combat this bias. Motivated by Pólya urn sampling, we sequentially update the set of potential donors with units already imputed, and use multiple imputations via Bayesian bootstrap to account for imputation uncertainty. Simulations with a single auxiliary variable show that our imputation method performs almost as well as competing methods with linear data, but better when data is nonlinear, especially with large samples. (original abstract)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu - Pełny tekst
- Pokaż
- Bibliografia
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- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng