Editing Imputation (statistics) (section)

===Non-negative matrix factorization===
{{main|Non-negative matrix factorization}}
[[Non-negative matrix factorization]] (NMF) can take missing data while minimizing its cost function, rather than treating these missing data as zeros that could introduce biases.<ref name = "ren20">{{Cite journal|arxiv=2001.00563|last1= Ren|first1= Bin |title= Using Data Imputation for Signal Separation in High Contrast Imaging|journal= The Astrophysical Journal|volume= 892|issue= 2|pages= 74|last2=  Pueyo|first2= Laurent|last3= Chen | first3 = Christine|last4=  Choquet|first4= Elodie |last5=  Debes|first5= John H|last6=  Duchene |first6= Gaspard|last7= Menard|first7=Francois|last8=Perrin|first8=Marshall D.|year= 2020|doi= 10.3847/1538-4357/ab7024 | bibcode = 2020ApJ...892...74R |s2cid= 209531731|doi-access= free}}</ref> This makes it a mathematically proven method for data imputation. NMF can ignore missing data in the cost function, and the impact from missing data can be as small as a second order effect.