The lay abstract featured today (for A new class of asymptotic maximin distance Latin hypercube designs by Xinxin Xia, Wenlong Li and Pengnan Li) is from The Canadian Journal of Statistics with the full article now available to read here.

How to Cite

Xia, X., Li, W. and Li, P. (2025), A new class of asymptotic maximin distance Latin hypercube designs. Can J Statistics. https://doi.org/10.1002/cjs.11836

Lay Abstract

This paper innovatively presents a new type of Latin hypercube designs with asymptotic maximin distance that exhibits remarkable uniformity in low-dimensional and high-dimensional spaces. The proposed designs play an important role in exploring black box models in computer experiments. For example, in the financial investment system, numerous factors are linked to form a complex black box model. The constructed designs can enable investors to better understand the actual mechanisms in the financial investment system through the exploration of the black box model, thereby making more informed investment decisions. In the field of deep neural networks, the proposed designs have the potential to promote hyperparameter selection and learning. With their good uniformity, they can comprehensively explore the hyperparameter space, improving the accuracy and generalization ability of the network, and providing a new idea for building powerful neural networks. From the perspective of big data sampling, as a robust method, the proposed designs can uniformly select samples from large-scale data. In this way, they can not only reduce the cost and time of data processing, but also significantly improve the accuracy and reliability of analysis. In summary, the designs proposed in this article have important application value in various aspects such as black box model research, hyperparameter selection and learning in deep neural networks and big data sampling.