Binary Response Regression

2021 ◽  
pp. 215-240
Author(s):  
Gary L. Rosner ◽  
Purushottam W. Laud ◽  
Wesley O. Johnson
Keyword(s):  
2013 ◽  
Vol 83 (1) ◽  
pp. 219-226 ◽  
Author(s):  
Gerhard Dikta ◽  
Sundarraman Subramanian ◽  
Thorsten Winkler

1983 ◽  
Vol 8 (1) ◽  
pp. 59-73 ◽  
Author(s):  
John E. Overall ◽  
Robert R. Starbuck

A binomial model is proposed for testing the significance of differences in binary response probabilities in two independent treatment groups. Without correction for continuity, the binomial statistic is essentially equivalent to Fisher’s exact probability. With correction for continuity, the binomial statistic approaches Pearson’s chi-square. Due to mutual dependence of the binomial and F distributions on the beta distribution, a simple F statistic can be used for computation instead of the binomial.


2016 ◽  
Vol 11 (1) ◽  
pp. 1600257 ◽  
Author(s):  
Kai Hu ◽  
Feng Teng ◽  
Lingxia Zheng ◽  
Pingping Yu ◽  
Zhiming Zhang ◽  
...  

1997 ◽  
Vol 26 (2) ◽  
pp. 229-236 ◽  
Author(s):  
Thomas H. Stevens ◽  
Christopher Barrett ◽  
Cleve E. Willis

Three conjoint models—a traditional ratings model, a ratings difference specification, and a binary response model—were used to value groundwater protection program alternatives. The last, which is virtually identical to a dichotomous choice contingent valuation specification, produced the smallest value estimates. This suggests that the conjoint model is very sensitive to model specification and that traditional conjoint models may overestimate economic value because many respondents are not in the market for the commodity being valued.


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