2026-10-14T11:38:48Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33071082 2026-09-17T13:06:32Z category_146 category_13 category_15 category_21 category_734 category_931 category_811 category_61 category_135 portal_87 item_type_3 month_year_09_2026

Grouped Latin Hypercube Designs with Controlled Correlations Wenlong Li (571749) Jian-Feng Yang (820139) Peter Chien (2360605) Space Science Genetics Neuroscience Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Developmental Biology Computational Biology Block orthogonalization Computer experiment Latin hypercube designs Multivariate regression Space-filling designs <p>Latin hypercube designs are widely used for running computer experiments. Motivated by modern computer experiments with grouped inputs, we propose a new type of Latin hypercube design, called <i>grouped Latin hypercube design with controlled correlations</i> (GLHD-CC). This special Latin hypercube partitions all columns into disjoint groups where controlled column-wise correlations across all inputs is achieved and inputs within the same group have lower column-wise correlations than those from different groups. Our construction method for this new design uses a multivariate block orthogonalization technique in multivariate linear regression to simultaneously control correlations across multiple variables. We present an efficient algorithm for enabling the construction method and establish its theoretical properties. The algorithm is flexible in run size and input dimension, with effectiveness demonstrated through three popular test functions and a case study on a true black-box simulator.</p> 2026-09-17T13:06:32Z Dataset Dataset 10.6084/m9.figshare.33071082.v2 https://figshare.com/articles/dataset/Grouped_Latin_Hypercube_Designs_with_Controlled_Correlations/33071082 CC BY 4.0