Missing data in optimal scaling

Pier Alda Ferrari and Paola Annoni

Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano

Abstract: We propose a procedure to assess a measure for a latent phenomenon, starting from the observation of a wide set of ordinal variables affected by structured missing data. The proposal is based on Nonlinear PCA technique to be jointly used with an ad hoc imputation method for the treatment of missing data. The procedure is particularly suitable when dealing with ordinal, or mixed, variables, which are strongly interrelated and in the presence of specific patterns of missing observations

Keywords: Nonlinear PCA; monotone missing data; ordinal variables; missing data passive (search for similar items in EconPapers)
Date: 2005-01-01
New Economics Papers: this item is included in nep-ecm and nep-mkt
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