Binary Response Models
Binary Response Models is a class of statistical regression models in categorical data analysis that describe the relationship between a set of explanatory variables and a single binary outcome by modeling the conditional probability of one of the two response categories, typically via an appropriate link function (such as logit or probit) in a generalized linear modeling framework.
901
Publications
101K
Citations
1.5K
Authors
715
Institutions
Publications per year
2017–2026
107
Variants
Binary Choice ModelsBinary Response AnalysisMultinomial Response ModelsNominal Response AnalysisOrdinal Response AnalysisOrdinal Response Models
Parents
901
Interaction terms in logit and probit models
Chunrong Ai, Edward C. Norton · Economics Letters · 2003 · 5.8K citations
John F. McDonald, Robert Moffitt · The Review of Economics and Statistics · 1980 · 1.8K citations
1–5 of 901
1.5K
16
h-index
16
h-index
12
h-index
12
h-index
10
h-index
1–5 of 1.5K
715
Harvard University
Cambridge, United States
25 h-index
58 publications
3.5K citations
University of Wisconsin–Madison
Madison, United States
16 h-index
25 publications
4.5K citations
University of Michigan
Ann Arbor, United States
14 h-index
28 publications
2K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
14 h-index
24 publications
7.6K citations
Cancer Research And Biostatistics
Seattle, United States
13 h-index
27 publications
1.9K citations
University of Washington
Seattle, United States
12 h-index
17 publications
1.1K citations
Dana-Farber Cancer Institute
Boston, United States
11 h-index
21 publications
2.2K citations

Johns Hopkins University
Baltimore, United States
11 h-index
17 publications
3.7K citations
1–8 of 715
58 publications · 10K citations
40
h-index
83 publications · 4.7K citations
38
h-index
66 publications · 9K citations
38
h-index
43 publications · 5K citations
30
h-index
26 publications · 3.7K citations
19
h-index
1–5