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ESTIMATING DISTRIBUTION FUNCTIONS FROM SURVEY DATA WITH LIMITED BENCHMARK INFORMATION
Author(s) -
Dunstan R.,
Chambers R. L.
Publication year - 1989
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1989.tb00493.x
Subject(s) - benchmark (surveying) , confidence interval , extension (predicate logic) , monte carlo method , computer science , statistics , variable (mathematics) , population , algorithm , data mining , mathematics , geography , mathematical analysis , demography , geodesy , sociology , programming language
summary The model‐based approach to estimation of finite population distribution functions introduced in Chambers & Dunstan (1986) is extended to the case where only summary information is available for the auxiliary size variable. Monte Carlo results indicate that this ‘limited information’ extension is almost as efficient as the ‘full information’ method proposed in the above reference. These results also indicate that the model‐based confidence intervals generated by either of these methods have superior coverage properties to more conventional design‐based confidence intervals.