Learning sparse conditional distribution: An efficient kernel-based approach

Fang Chen, Xin He, Junhui Wang
2021 Electronic Journal of Statistics  
This paper proposes a novel method to recover the sparse structure of the conditional distribution, which plays a crucial role in subsequent statistical analysis such as prediction, forecasting, conditional distribution estimation and others. Unlike most existing methods that often require explicit model assumption or suffer from computational burden, the proposed method shows great advantage by making use of some desirable properties of reproducing kernel Hilbert space (RKHS). It can be
more » ... ntly implemented by optimizing its dual form and is particularly attractive in dealing with large-scale dataset. The asymptotic consistencies of the proposed method are established under mild conditions. Its effectiveness is also supported by a variety of simulated examples and a real-life supermarket dataset from Northern China. MSC2020 subject classifications: Primary 68Q32, 62G08; secondary 62J07.
doi:10.1214/21-ejs1824 fatcat:lckc4v4drrhtjggsqtjqkda6se