Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting

The paper addresses the challenge of estimating joint probability distributions in high-dimensional settings by factorizing them autoregressively and using nonparametric conditional estimators. It establishes theoretical convergence bounds that link the difficulty of joint estimation to the hardest factor in the factorization, especially when conditional estimators adapt to sparsity in an undirected dependence graph. Empirical results show that an autoregressive estimator built with parallel gradient boosting achieves competitive performance on simulated and real datasets, even without optimizing factorization order.

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