Statistical Matching: Theory and Practice (Wiley Series in Survey Methodology)

By Marcello D'Orazio, Marco Di Zio, Mauro Scanu. 2006

326 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5439a23592d504eb3e87d8af5f6d73eda2f95380

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Steen Magnussen
2. Ronald E. McRoberts
3. Erkki Tomppo

In Management Science and Operations Research

1. Steen Magnussen
2. Ronald E. McRoberts
3. Erkki Tomppo
4. S. van Buuren

In Signal Processing

1. Steen Magnussen
2. Ronald E. McRoberts
3. Erkki Tomppo

Citations in the corpus

• Data analysis across various media: Data fusion, direct marketing, clickstream data and social media (2011, background): “By feeding this learning back into the organization, this becomes an ever more powerful, adaptive mode of interacting with your customers.”
• Flexible Imputation of Missing Data (2012, background): “There is a considerable literature on matching methods in the context of observational studies and file matching where this problem is recognized (Rubin, 2006; D’Orazio et al., 2006).”
• Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009, methodology): “A clever use of this ancillary information for stratification, regression, or calibration can reduce the probability-based mean square error (MSE) of desired estimates (D'Orazio et al., 2006; Wu, 2003).”