title: On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix.
abstract: We investigate the maximal size of distinguished submatrices of a Gaussian random matrix. Of interest are submatrices whose entries have an average greater than or equal to a positive constant, and submatrices whose entries are well fit by a two-way ANOVA model. We identify size thresholds and associated (asymptotic) probability bounds for both large-average and ANOVA-fit submatrices. Probability bounds are obtained when the matrix and submatrices of interest are square and, in rectangular cases, when the matrix and submatrices of interest have fixed aspect ratios. Our principal result is an almost sure interval concentration result for the size of large average submatrices in the square case.
authors.affiliation: Merck & Co., Inc., One Merck Drive, Whitehouse Station, NJ 08889, USA.
authors.forename: Xing
authors.initials: X
authors.lastname: Sun
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Nobel
keywords: Gaussian random matrix; analysis of variance; data mining; large average submatrix; random matrix theory; second moment method