Absolute Continuity of Monotone Aggregations under Positive Regression Dependence
This study addresses the absolute continuity of the distribution of outputs generated by monotone aggregation functions under positively regression-dependent structures. By introducing stochastic order monotonicity of conditional distributions and coordinate-wise monotonicity of mappings, the authors establish sufficient conditions that do not require independence or joint density assumptions, allowing for arbitrary marginal distributions. The results are extended to general measurable spaces equipped with reflexive binary relations. Leveraging tools from stochastic orders, conditional distributions, measure theory, and monotone functions, the paper proves the absolute continuity of the distribution of the aggregated variable \(g(X,Y)\). This extends the applicability of convolution regularization techniques and provides theoretical foundations for applications such as risk aggregation.