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Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm

Antonio D’Ambrosio; Aria, Massimo; Siciliano, Roberta - In: Journal of Classification 29 (2012) 2, pp. 227-258

Persistent link: https://www.econbiz.de/10010558351

Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis

Ginkel, Joost; Kroonenberg, Pieter - In: Journal of Classification 31 (2014) 2, pp. 242-269

Multiple imputation is one of the most highly recommended procedures for dealing with missing data. However, to date little attention has been paid to methods for combining the results from principal component analyses applied to a multiply imputed data set. In this paper we propose Generalized...

Persistent link: https://www.econbiz.de/10010950404

Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models

Hattum, Pascal; Hoijtink, Herbert - In: Journal of Classification 26 (2009) 3, pp. 297-328

Persistent link: https://www.econbiz.de/10008591070

Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings

Busing, Frank; Rooij, Mark - In: Journal of Classification 26 (2009) 3, pp. 329-360

Persistent link: https://www.econbiz.de/10008591074

Brossier, Gildas - In: Journal of Classification 11 (1994) 1, pp. 37-58

Persistent link: https://www.econbiz.de/10005602931