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10.1016/j.socnet.2015.12.003 http://scihub22266oqcxt.onion/10.1016/j.socnet.2015.12.003 C4743534!4743534 !26858508     free     free     free       Soc+Networks 2016 ; 45 (?): 89-98 Nephropedia Template TP {{tp|p=26858508 |t=2016. Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health |pdf=|usr=}}{{26858508 }} gab.com Text Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health JO: Soc Networks http://www.kidney.de/pget.php?&tw=1&pmid=26858508 #FOAvip Recent developments have made model-based imputation of network data feasible in principle, but the extant literature provides few practical examples of its use. In this paper we consider 14 schools from the widely used In-School Survey of Add Health (Harris et al., 2009), applying an ERGM-based estimation and simulation approach to impute the network missing data for each school. Add Health's complex study design leads to multiple types of missingness, and we introduce practical techniques for handing each. We also develop a cross-validation based method - Held-Out Predictive Evaluation (HOPE) - for assessing this approach. Our results suggest that ERGM-based imputation of edge variables is a viable approach to the analysis of complex studies such as Add Health, provided that care is used in understanding and accounting for the study design. Twit Text FOAVip Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health ????Soc Networks http://www.kidney.de/pget.php?&tw=1&pmid=26858508 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26858508 #FOAvip English Wikipedia <ref>{{Cite pmid|26858508 }}</ref> Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health #MMPMID26858508 Wang C ; Butts CT ; Hipp JR ; Jose R ; Lakon CM Soc Networks 2016[Mar]; 45 (?): 89-98 PMID26858508 show ga Recent developments have made model-based imputation of network data feasible in principle, but the extant literature provides few practical examples of its use. In this paper we consider 14 schools from the widely used In-School Survey of Add Health (Harris et al., 2009), applying an ERGM-based estimation and simulation approach to impute the network missing data for each school. Add Health's complex study design leads to multiple types of missingness, and we introduce practical techniques for handing each. We also develop a cross-validation based method - Held-Out Predictive Evaluation (HOPE) - for assessing this approach. Our results suggest that ERGM-based imputation of edge variables is a viable approach to the analysis of complex studies such as Add Health, provided that care is used in understanding and accounting for the study design. ?Multiple Imputation for Missing Edge Data: A Predictive Evaluation Met(epmc).pdf DeepDyve Pubget Overpricing