چکیده : 

In the analysis of contingency tables, the odds ratio is a measure commonly used to

summarize the strength of association between two categorical variables, say R and S.

When a vector of continuous variables X is also observed for each individual in the table,

then it is important to analyze whether and how the degree of association (odds ratio)

varies locally with X. In this article, several nonparametric estimators of this conditional or

local odds ratio are proposed, to summarize the strength of local association between R and

S given X. The nonparametric estimators are constructed using kernel regression, to allow

for maximum flexibility. Confidence intervals based on these nonparametric estimators

are also developed. Simulation studies show that our proposed (amended) local odds ratio

estimators can outperform the model-based counterparts from logistic regression and

Generalized Additive Models, without the need for a linearity or additivity assumption