Novel 2-D graphical representation of proteins

2006 ◽  
Vol 419 (4-6) ◽  
pp. 528-532 ◽  
Author(s):  
Milan Randić ◽  
Darko Butina ◽  
Jure Zupan
2011 ◽  
Vol 111 (2) ◽  
pp. 790-862 ◽  
Author(s):  
Milan Randić ◽  
Jure Zupan ◽  
Alexandru T. Balaban ◽  
Dražen Vikić-Topić ◽  
Dejan Plavšić

2009 ◽  
Vol 27 (5) ◽  
pp. 637-641 ◽  
Author(s):  
Milan Randić ◽  
Ketij Mehulić ◽  
Damir Vukičević ◽  
Tomaž Pisanski ◽  
Dražen Vikić-Topić ◽  
...  

2012 ◽  
Vol 391 (1-2) ◽  
pp. 93-99 ◽  
Author(s):  
Ping-an He ◽  
Jinzhou Wei ◽  
Yuhua Yao ◽  
Zhixin Tie

2007 ◽  
Author(s):  
Haihua Bai ◽  
Chun Li ◽  
Hasi Agula ◽  
Jirimutu ◽  
Jun Wang ◽  
...  

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.


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