Algorithm 633: An algorithm for linear dependency analysis of multivariate data

The importance of detecting statistical dependencies in multivariate data has been discussed many times (e.g., Belsley et al. [l]). Recently, Kane et al. [5] have developed a procedure called Linear Dependency Analysis (LDA), which assesses the existence of linear dependencies in a multivariate data matrix X. This paper describes the algorithm implementing the LDA procedure. A brief description of some of the statistical and linear algebra theory behind the procedure is given below for notational purposes. Kane et al. [5] should be consulted for additional details and discussion of the procedure’s theoretical foundations. LDA examines the potential for partitioning the R. X p matrix X into the n X p1 matrix X1 containing the predictor variables and the n x p2 matrix X2 containing the estimated variables, and the appropriateness of using the linear relationship