wake garg latin hypercube sampling informs

#Wake garg latin hypercube sampling informs full#

#Wake garg latin hypercube sampling informs full#

References: View references in EconPapers View complete reference list from CitEcĪccess to full text is restricted to subscribers. We also present numerical studies that showcase the flexible incrementation offered by HILHS. We derive the sampling properties for HLHS designs and HILHS estimators. The intermediate designs on the spectrum are defined as Partially Stratified Sample designs and are shown to reduce variance associated with variable interactions whereas LHS reduces the variance associated with the main effects of the transformation. This overcomes a drawback of many LHS schemes that require the entire sample set to be selected a priori, or only allow very large increments. In this work, the Latin hypercube sampling method of has been generalized by defining a spectrum of stratified sampling methods of which true stratified sampling and Latin hypercube sampling lie at its extremes. Based on this new construction, we introduce a hierarchical incremental LHS (HILHS) method that allows the user to employ LHS in a flexibly incremental setting. We note Xn j the n-sample of the variableXj. Let the range of each variable Xj, j 1.p, be simultaneously partitioned into n equally probable intervals. LHS ensures that each of the input variables has all of its range represented. We present a new algorithm for generating hierarchic Latin hypercube sets (HLHS) that are recursively divisible into LHS subsets. Latin hypercube sampling (LHS) which is an extension of stratied sampling.

Latin hypercube sampling (LHS) is a robust, scalable Monte Carlo method that is used in many areas of science and engineering. Journal of the American Statistical Association, 2017, vol.