StatMatch: Statistical Matching. Integration of two data sources referred to the same target population which share a number of common variables (aka data fusion). Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.

References in zbMATH (referenced in 11 articles )

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  1. Mark D. Risser, Catherine A. Calder: Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R (2015) arXiv
  2. Roesch, Andreas; Lips, Markus: Sampling design for two combined samples of the farm accountancy data network (FADN) (2013)
  3. Ruiz Espejo, Mariano; Delgado Pineda, Miguel; Nadarajah, Saralees: Optimal unbiased estimation of some population central moments (2013)
  4. Brozzi, Alessandro; Capotorti, Andrea; Vantaggi, Barbara: Incoherence correction strategies in statistical matching (2012)
  5. D’Ambrosio, Antonio; Aria, Massimo; Siciliano, Roberta: Accurate tree-based missing data imputation and data fusion within the statistical learning paradigm (2012)
  6. Fienberg, Stephen E.: Bayesian models and methods in public policy and government settings (2011)
  7. Conti, Pier Luigi; Marella, Daniela; Scanu, Mauro: Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators (2008)
  8. Marella, Daniela; Scanu, Mauro; Conti, Pier Luigi: On the matching noise of some nonparametric imputation procedures (2008)
  9. Vantaggi, Barbara: Statistical matching of multiple sources: A look through coherence (2008)
  10. Aluja-Banet, Tomàs; Daunis-I-Estadella, Josep; Pellicer, David: GRAFT, a complete system for data fusion (2007)
  11. D’Orazio, Marcello D.; Di Zio, Marco; Scanu, Mauro: Statistical matching. Theory and practice (2006)