Graphics Reference
In-Depth Information
80. Rubin, D.B.: Multiple Imputation for Nonresponse in Surveys. Wiley, New York (1987)
81. Safarinejadian, B., Menhaj, M., Karrari, M.: A distributed EM algorithm to estimate the
parameters of a finite mixture of components. Knowl. Inf. Syst.
23
(3), 267-292 (2010)
82. Schafer, J.L.: Analysis of Incomplete Multivariate Data. Chapman & Hall, London (1997)
83. Schafer, J.L., Olsen, M.K.: Multiple imputation for multivariate missing-data problems: a
data analyst's perspective. Multivar. Behav. Res.
33
(4), 545-571 (1998)
84. Scheuren, F.: Multiple imputation: how it began and continues. Am. Stat.
59
, 315-319 (2005)
85. Schneider, T.: Analysis of incomplete climate data: estimation of mean values and covariance
matrices and imputation of missing values. J. Clim.
14
, 853-871 (2001)
86. Schomaker,M., Heumann, C.:Model selection andmodel averaging after multiple imputation.
Comput. Stat. Data Anal.
71
, 758-770 (2014)
87. Sehgal, M.S.B., Gondal, I., Dooley, L.: Collateral missing value imputation: a new robust
missing value estimation algorithm for microarray data. Bioinform.
21
(10), 2417-2423 (2005)
88. Silva-Ramírez, E.L., Pino-Mejías, R., López-Coello, M., Cubiles-de-la Vega, M.D.: Missing
value imputation on missing completely at random data using multilayer perceptrons. Neural
Networks
24
(1), 121-129 (2011)
89. Simnski, K.: Rough fuzzy subspace clustering for data with missing values. Comput. Inform.
33
(1), 131-153 (2014)
90. Somasundaram, R., Nedunchezhian, R.: Radial basis function network dependent exclusive
mutual interpolation for missing value imputation. J. Comput. Sci.
9
(3), 327-334 (2013)
91. Tanner, M.A., Wong, W.: The calculation of posterior distributions by data augmentation. J.
Am. Stat. Assoc.
82
, 528-540 (1987)
92. Ting, J., Yu, B., Yu, D., Ma, S.: Missing data analyses: a hybrid multiple imputation algorithm
using gray system theory and entropy based on clustering. Appl. Intell.
40
(2), 376-388 (2014)
93. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein,
D., Altman, R.B.: Missing value estimation methods for dna microarrays. Bioinform.
17
(6),
520-525 (2001)
94. Unnebrink, K., Windeler, J.: Intention-to-treat: methods for dealing with missing values in
clinical trials of progressively deteriorating diseases. Stat. Med.
20
(24), 3931-3946 (2001)
95. Vellido, A.: Missing data imputation through GTM as a mixture of t-distributions. Neural
Networks
19
(10), 1624-1635 (2006)
96. Wang, H., Wang, S.: Mining incomplete survey data through classification. Knowl. Inf. Syst.
24(2), 221-233 (2010)
97. Williams, D., Liao, X., Xue, Y., Carin, L., Krishnapuram, B.: On classificationwith incomplete
data. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 427-436 (2007)
98. Wilson, D.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans.
Syst. Man Cybern.
2
(3), 408-421 (1972)
99. Wong, A.K.C., Chiu, D.K.Y.: Synthesizing statistical knowledge from incomplete mixed-
mode data. IEEE Trans. Pattern Anal. Mach. Intell.
9
(6), 796-805 (1987)
100. Wu, X., Urpani, D.: Induction by attribute elimination. IEEE Trans. Knowl. Data Eng.
11
(5),
805-812 (1999)
101. Zhang, S.: Nearest neighbor selection for iteratively knn imputation. J. Syst. Softw.
85
(11),
2541-2552 (2012)
102. Zhang, S., Wu, X., Zhu, M.: Efficient missing data imputation for supervised learning. In:
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on, pp. 672-679 (2010)
103. Zheng, Z.,Webb, G.I.: Lazy learning of bayesian rules. Machine Learning
41
(1), 53-84 (2000)
104. Zhu, B., He, C., Liatsis, P.: A robust missing value imputation method for noisy data. Appl.
Intell.
36
(1), 61-74 (2012)
105. Zhu, X., Zhang, S., Jin, Z., Zhang, Z., Xu, Z.: Missing value estimation for mixed-attribute
data sets. IEEE Transactions on Knowl. Data Eng.
23
(1), 110-121 (2011)
