5 papers
Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks
Nicola Rares Franco, Lorenzo Tedesco
We introduce conditional push-forward neural networks (CPFN), a generative framework for conditional distribution estimation. Instead of directly modeling the conditional density $…
Multivariate Low-Rank State-Space Model with SPDE Approach for High-Dimensional Data
Jacopo Rodeschini, Lorenzo Tedesco, Francesco Finazzi +2
This paper proposes a novel low-rank approximation to the multivariate State-Space Model. The Stochastic Partial Differential Equation (SPDE) approach is applied component-wise to…
Instrumental variable estimation of the proportional hazards model by presmoothing
Lorenzo Tedesco, Jad Beyhum, Ingrid Van Keilegom
We consider instrumental variable estimation of the proportional hazards model of Cox (1972). The instrument and the endogenous variable are discrete but there can be (possibly con…
Comparison of Quantile Regression Curves with Censored Data
Lorenzo Tedesco, Ingrid Van Keilegom
This paper proposes a new test for the comparison of conditional quantile curves when the outcome of interest, typically a duration, is subject to right censoring. The test can be…
Instrumental variable quantile regression under random right censoring
Jad Beyhum, Lorenzo Tedesco, Ingrid Van Keilegom
This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It…