… outcome of any experiment which is a mixture of component experiments should be interpreted
in the same way as if it were an outcome of … basis from which statistical inferences can be …

… This article develops a unified statistical inference framework for high-dimensional binary …
of independent interest to solve other inference problems in high-dimensional binary GLMs. …

… the average effect of treatment on a binary outcome without assuming additivity. These sets
… and subsequently a binary outcome is measured. Let the binary outcome of interest be …

… A regression with a binary outcome y presents special difficulties. … when the endogenous
regressor of interest is also binary. … With a binary outcome Y, we can write a “threshold model” …

… According to the likelihood principle (L), when any binary experiment E gives any outcome x
de… of X(x) is the same as that of the positive outcome of the symmetric simple binary experi- …

… We propose a decision rule for binary outcome similar to Liu et al. (Citation2002). Given the
data from the bridging study and prior information on t and c from the reference studies, the …

… binary outcomes framing our discussion in the potential outcome approach to causal inference.
… of interest, we introduce new binary outcomes, defined as function (product) of subsets of …

… In this article, I summarize the relevant literature on regression analysis of binary outcomes,
… of simple and multiple linear regression to estimate treatment effects on binary outcomes.1 …

… Statistical inference provides the link between the study sample and the source population …
statistics must be used to provide valid inferences about the relationship between an outcome …

… In this article, we discuss a likelihood-based inference of the AC 1 in the case of two raters
with binary outcomes. Construction of confidence intervals is mainly discussed. In addition, …