Conditional Rank-Rank Regression via Deep Conditional Transformation Models
Intergenerational mobility quantifies the transmission of socio-economic outcomes from parents to children. While rank-rank regression (RRR) is standard, adding covariates directly (RRRX) often yields parameters with unclear interpretation. Conditional rank-rank regression (CRRR) resolves this by using covariate-adjusted (conditional) ranks to measure within-group mobility. We improve and extend CRRR by estimating conditional ranks with a deep conditional transformation model (DCTM) and cross-fitting, enabling end-to-end conditional distribution learning with structural constraints and strong performance under nonlinearity, high-order interactions, and discrete ordered outcomes where the ...
| Authors | Xiaoyi Wang, Long Feng, Zhaojun Wang |
|---|---|
| Primary category | stat.ME |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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