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  • 1. Small domain estimation for unequal probability survey designs Archived
    Articles and reports: 12-001-X199400114435
    Description:

    The problem of estimating domain totals and means from sample survey data is common. When the domain is large, the observed sample is generally large enough that direct, design-based estimators are sufficiently accurate. But when the domain is small, the observed sample size is small and direct estimators are inadequate. Small area estimation is a particular case in point and alternative methods such as synthetic estimation or model-based estimators have been developed. The two usual facets of such methods are that information is ‘borrowed’ from other small domains (or areas) so as to obtain more precise estimators of certain parameters and these are then combined with auxiliary information, such as population means or totals, from each small area in turn to obtain a more precise estimate of the domain (or area) mean or total. This paper describes a case involving unequal probability sampling in which no auxiliary population means or totals are available and borrowing strength from other domains is not allowed and yet simple model-based estimators are developed which appear to offer substantial efficiency gains. The approach is motivated by an application to market research but the methods are more widely applicable.

    Release date: 1994-06-15
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  • 1. Small domain estimation for unequal probability survey designs Archived
    Articles and reports: 12-001-X199400114435
    Description:

    The problem of estimating domain totals and means from sample survey data is common. When the domain is large, the observed sample is generally large enough that direct, design-based estimators are sufficiently accurate. But when the domain is small, the observed sample size is small and direct estimators are inadequate. Small area estimation is a particular case in point and alternative methods such as synthetic estimation or model-based estimators have been developed. The two usual facets of such methods are that information is ‘borrowed’ from other small domains (or areas) so as to obtain more precise estimators of certain parameters and these are then combined with auxiliary information, such as population means or totals, from each small area in turn to obtain a more precise estimate of the domain (or area) mean or total. This paper describes a case involving unequal probability sampling in which no auxiliary population means or totals are available and borrowing strength from other domains is not allowed and yet simple model-based estimators are developed which appear to offer substantial efficiency gains. The approach is motivated by an application to market research but the methods are more widely applicable.

    Release date: 1994-06-15
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