The data available for estimating welfare indicators are often inconveniently incomplete data: they may be censored or truncated. Furthermore, for robustness reasons, researchers sometimes use trimmed samples. By using the statistical tool known as the Influence Function we derive distribution-free asymptotic variances for wide classes of welfare indicators not only in the complete data case, but also in the important cases where the data have been trimmed, censored or truncated.
Distribution-Free Inference for Welfare Indices under Complete and Incomplete Information / Frank A. Cowell; Maria-Pia Victoria-Feser. - In: THE JOURNAL OF ECONOMIC INEQUALITY. - ISSN 1569-1721. - STAMPA. - 1:3(2003), pp. 191-219. [10.1023/b:joei.0000004637.12354.c7]
Distribution-Free Inference for Welfare Indices under Complete and Incomplete Information
Maria-Pia Victoria-Feser
2003
Abstract
The data available for estimating welfare indicators are often inconveniently incomplete data: they may be censored or truncated. Furthermore, for robustness reasons, researchers sometimes use trimmed samples. By using the statistical tool known as the Influence Function we derive distribution-free asymptotic variances for wide classes of welfare indicators not only in the complete data case, but also in the important cases where the data have been trimmed, censored or truncated.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
