-
1
-
-
84924288258
-
-
Stanford University and the University of Michigan [producers]
-
American National Election Studies (ANES; www.electionstudies.org). The ANES 2008 Time Series Study [data set]. Stanford University and the University of Michigan [producers].
-
The ANES 2008 Time Series Study [data set]
-
-
-
2
-
-
33847711413
-
Robustness of a multivariate normal approximation for imputation of incomplete binary data
-
Bernaards, Coen A., Thomas R. Belin, and Joseph L. Schafer. 2007. Robustness of a multivariate normal approximation for imputation of incomplete binary data. Statistics in Medicine 26(6):1368-82.
-
(2007)
Statistics in Medicine
, vol.26
, Issue.6
, pp. 1368-1382
-
-
Bernaards, C.A.1
Belin, T.R.2
Schafer, J.L.3
-
3
-
-
84875140733
-
We have to be discrete about this: A non-parametric imputation technique for missing categorical data
-
Cranmer, Skyler J., and Jeff Gill. 2013. We have to be discrete about this: A non-parametric imputation technique for missing categorical data. British Journal of Political Science 43(2):425-49.
-
(2013)
British Journal of Political Science
, vol.43
, Issue.2
, pp. 425-449
-
-
Cranmer, S.J.1
Gill, J.2
-
5
-
-
77649115914
-
A distance-based rounding strategy for post-imputation ordinal data
-
Demirtas, Hakan. 2010. A distance-based rounding strategy for post-imputation ordinal data. Journal of Applied Statistics 37(3):489-500.
-
(2010)
Journal of Applied Statistics
, vol.37
, Issue.3
, pp. 489-500
-
-
Demirtas, H.1
-
6
-
-
85015544392
-
Maximum likelihood from incomplete data via the EM algorithm
-
Dempster, Arthur P., Nan Laird, and Donald B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 39(1):1-38.
-
(1977)
Journal of the Royal Statistical Society, Series B (Methodological)
, vol.39
, Issue.1
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.2
Rubin, D.B.3
-
7
-
-
84865371361
-
A weakly informative default prior distribution for logistic and other regression models
-
Gelman, Andrew, Aleks Jakulin, Maria Grazia Pittau, and Yu-Sung Su. 2008. A weakly informative default prior distribution for logistic and other regression models. Annals of Applied Statistics 2(4):1360-83.
-
(2008)
Annals of Applied Statistics
, vol.2
, Issue.4
, pp. 1360-1383
-
-
Gelman, A.1
Jakulin, A.2
Pittau, M.G.3
Su, Y.-S.4
-
8
-
-
84867706136
-
-
R package version 1.5-05
-
Gelman, Andrew, Yu-Sung Su, Masanao Yajima, Jennifer Hill, Maria Grazia Pittau, Jouni Kerman, and Tian Zheng. 2012. arm: Data analysis using regression and multilevel/hierarchical models. R package version 1.5-05. http://CRAN.Rproject.org/package1/4arm.
-
(2012)
arm: Data analysis using regression and multilevel/hierarchical models
-
-
Gelman, A.1
Su, Y.-S.2
Yajima, M.3
Hill, J.4
Pittau, M.G.5
Kerman, J.6
Zheng, T.7
-
10
-
-
0029584587
-
A critical look at methods for handling missing covariates in epidemiologic regression analyses
-
Greenland, Sander, and William D. Finkle. 1995. A critical look at methods for handling missing covariates in epidemiologic regression analyses. American Journal of Epidemiology 142(12):1255-64.
-
(1995)
American Journal of Epidemiology
, vol.142
, Issue.12
, pp. 1255-1264
-
-
Greenland, S.1
Finkle, W.D.2
-
11
-
-
77954332545
-
What to do about missing values in time-series cross-section data
-
Honaker, James, and Gary King. 2010. What to do about missing values in time-series cross-section data. American Journal of Political Science 54(2):561-81.
-
(2010)
American Journal of Political Science
, vol.54
, Issue.2
, pp. 561-581
-
-
Honaker, J.1
King, G.2
-
14
-
-
0242710940
-
A potential for bias when rounding in multiple imputation
-
Horton, Nicholas J., Stuart R. Lipsitz, and Michael Parzen. 2003. A potential for bias when rounding in multiple imputation. American Statistician 57(4):229-32.
-
(2003)
American Statistician
, vol.57
, Issue.4
, pp. 229-232
-
-
Horton, N.J.1
Lipsitz, S.R.2
Parzen, M.3
-
16
-
-
77249147857
-
Multiple imputation for missing data: Fully conditional specification versus multivariate normal imputation
-
Lee, Katherine J., and John B. Carlin. 2010. Multiple imputation for missing data: Fully conditional specification versus multivariate normal imputation. American Journal of Epidemiology 171(5):624-32.
-
(2010)
American Journal of Epidemiology
, vol.171
, Issue.5
, pp. 624-632
-
-
Lee, K.J.1
Carlin, J.B.2
-
17
-
-
68949200946
-
Generating random correlation matrices based on vines and extended onion method
-
Lewandowski, Daniel, Dorota Kurowicka, and Harry Joe. 2010. Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis 100(9):1989-2001.
-
(2010)
Journal of Multivariate Analysis
, vol.100
, Issue.9
, pp. 1989-2001
-
-
Lewandowski, D.1
Kurowicka, D.2
Joe, H.3
-
19
-
-
32444450470
-
Multiple imputation of missing values: Update
-
Royston, Patrick. 2005. Multiple imputation of missing values: Update. Stata Journal 5(2):188-201.
-
(2005)
Stata Journal
, vol.5
, Issue.2
, pp. 188-201
-
-
Royston, P.1
-
20
-
-
43749105785
-
Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring
-
Royston, Patrick. 2007. Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring. Stata Journal 7(4):445-74.
-
(2007)
Stata Journal
, vol.7
, Issue.4
, pp. 445-474
-
-
Royston, P.1
-
21
-
-
70349590221
-
Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables
-
Royston, Patrick. 2009. Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. Stata Journal 9(3):466-77.
-
(2009)
Stata Journal
, vol.9
, Issue.3
, pp. 466-477
-
-
Royston, P.1
-
23
-
-
84940952808
-
Statistical matching using file concatenation with adjusted weights and multiple imputations
-
Rubin, Donald B. Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business and Economic Statistics 4(1):87-94.
-
Journal of Business and Economic Statistics
, vol.4
, Issue.1
, pp. 87-94
-
-
Rubin, D.B.1
-
27
-
-
0032219074
-
Multiple imputation for multivariate missing-data problems: A data analyst's perspective
-
Schafer, Joseph L., and Maren K. Olsen. 1998. Multiple imputation for multivariate missing-data problems: A data analyst's perspective. Multivariate Behavioral Research 33(4):545-71.
-
(1998)
Multivariate Behavioral Research
, vol.33
, Issue.4
, pp. 545-571
-
-
Schafer, J.L.1
Olsen, M.K.2
-
28
-
-
84899623960
-
-
College Station, TX: Stata Press
-
StataCorp. 2013. Stata 13 base reference manual. College Station, TX: Stata Press.
-
(2013)
Stata 13 base reference manual
-
-
-
29
-
-
84863414529
-
Multiple imputation with diagnostics (mi) in R: Opening windows into the black box
-
Su, Yu-Sung, Andrew Gelman, Jennifer Hill, and Masanao Yajima. 2011. Multiple imputation with diagnostics (mi) in R: Opening windows into the black box. Journal of Statistical Software 45(2):1-31.
-
(2011)
Journal of Statistical Software
, vol.45
, Issue.2
, pp. 1-31
-
-
Su, Y.-S.1
Gelman, A.2
Hill, J.3
Yajima, M.4
-
31
-
-
34347407592
-
Multiple imputation of discrete and continuous data by fully conditional specification
-
van Buuren, Stef. 2007. Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research 16(3):219-42.
-
(2007)
Statistical Methods in Medical Research
, vol.16
, Issue.3
, pp. 219-242
-
-
van Buuren, S.1
-
33
-
-
0033616909
-
Multiple imputation of missing blood pressure covariates in survival analysis
-
van Buuren, Stef, Hendriek C. Boshuizen, and D. L. Knook. 1999. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine 18(6):681-94.
-
(1999)
Statistics in Medicine
, vol.18
, Issue.6
, pp. 681-694
-
-
van Buuren, S.1
Boshuizen, H.C.2
Knook, D.L.3
|