Handling Missing Data
Handling Missing Data is a methodological framework within the domain of exponential smoothing that systematically addresses the absence of observations in time series by imputing or adjusting for missing values through adaptive smoothing mechanisms.
1K
Publications
66.6K
Citations
2.7K
Authors
1.1K
Institutions
Publications per year
2017–2026
394
Parents
1K
Analysis of Incomplete Multivariate Data
David E. Booth, Joseph L. Schafer · Technometrics · 2000 · 5.7K citations
1–5 of 1K
2.7K
9
h-index
7
h-index
7
h-index
6
h-index
6
h-index
1–5 of 2.7K
1.1K
University of Chicago
Chicago, United States
14 h-index
20 publications
1.2K citations
University of California, Los Angeles
Los Angeles, United States
13 h-index
18 publications
1.5K citations
University of Florida
Gainesville, United States
12 h-index
27 publications
1.5K citations

Pennsylvania State University
State College, United States
11 h-index
23 publications
10.2K citations
University of Cambridge
Cambridge, United Kingdom
10 h-index
16 publications
725 citations
Harvard University
Cambridge, United States
10 h-index
14 publications
1.1K citations

Delft University of Technology
Delft, The Netherlands
10 h-index
19 publications
902 citations
Tsinghua University
Beijing, China
10 h-index
26 publications
4.2K citations
1–8 of 1.1K
41 publications · 3.1K citations
26
h-index
14 publications · 469 citations
14
h-index
CSDA · 18 publications · 1.5K citations
14
h-index
Repository · 14 publications · 1.1K citations
13
h-index
11 publications · 805 citations
11
h-index
1–5