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Complete case analysis 1 Missing data 1 Multiple imputation 1 Random Forests 1 Variable importance 1 Variable selection 1 complete case analysis 1 direct likelihood 1 dropout 1 imputation 1 missing at random 1 missing completely at random 1 missing data 1
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Gerris, Jan R. M. 1 Hapfelmeier, A. 1 Jansen, Ivy 1 Molenberghs, Geert 1 Troost, Ann Van den 1 Ulm, K. 1 Vermulst, Ad A. 1
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Computational Statistics & Data Analysis 1 Sociological Methods & Research 1
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Variable selection by Random Forests using data with missing values
Hapfelmeier, A.; Ulm, K. - In: Computational Statistics & Data Analysis 80 (2014) C, pp. 129-139
predictor variables. Possible solutions are multiple imputation, complete case analysis and the use of a self … recommendations: Complete case analysis should not be used as it led to inaccurate variable selection. Multiple imputation is the …
Persistent link: https://www.econbiz.de/10010906927
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Modeling Partially Incomplete Marital Satisfaction Data
Jansen, Ivy; Troost, Ann Van den; Molenberghs, Geert; … - In: Sociological Methods & Research 35 (2006) 1, pp. 113-136
The authors analyze data on marital satisfaction, obtained from couples at two distinct moments in time (1990, 1995). The data are of a bivariate longitudinal type. Moreover, some couples provide incomplete records only, usually because the 1995 follow-up interview has not taken place. The...
Persistent link: https://www.econbiz.de/10010789689
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