caa a22 4500 606196447 CHVBK 20210128100924.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10260-014-0290-7 doi (NATIONALLICENCE)springer-10.1007/s10260-014-0290-7 Further results on orthogonal arrays for the estimation of global sensitivity indices based on alias matrix [Elektronische Daten] [Xue-ping Chen, Jin-Guan Lin, Xiao-di Wang, Xing-fang Huang] In this paper, the use of orthogonal arrays with strength $$s<p,$$ s < p , where $$p$$ p is the required strength, for global sensitivity analysis is considered. We first generalize the alias matrix for ANOVA high-dimensional model representation based on matrix image, and then by sequentially minimizing the squared alias degrees, we present a approach for the estimation of sensitivity indices. A two-level orthogonal array with 16 runs and a four-level orthogonal array with 64 runs are studied for estimating both low-order and high-order significant sensitivity indices. Moreover, models containing larger than 10 input factors are also investigated. All cases show that designs with smaller squared alias degree have less bias and variance for the estimations of global sensitivity indices. Springer-Verlag Berlin Heidelberg, 2014 Alias matrix nationallicence Matrix image nationallicence ANOVA HDMR nationallicence Strength nationallicence Global sensitivity indices nationallicence Chen Xue-ping Department of Mathematics, Southeast University, 211189, Nanjing, China aut Lin Jin-Guan Department of Mathematics, Southeast University, 211189, Nanjing, China aut Wang Xiao-di School of Statistics, Central University of Finance and Economics, 100081, Beijing, China aut Huang Xing-fang Department of Mathematics, Southeast University, 211189, Nanjing, China aut Statistical Methods & Applications Springer Berlin Heidelberg 24/3(2015-09-01), 411-426 1618-2510 24:3<411 2015 24 10260 https://doi.org/10.1007/s10260-014-0290-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-014-0290-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Xue-ping Department of Mathematics, Southeast University, 211189, Nanjing, China aut NATIONALLICENCE 700 1- Lin Jin-Guan Department of Mathematics, Southeast University, 211189, Nanjing, China aut NATIONALLICENCE 700 1- Wang Xiao-di School of Statistics, Central University of Finance and Economics, 100081, Beijing, China aut NATIONALLICENCE 700 1- Huang Xing-fang Department of Mathematics, Southeast University, 211189, Nanjing, China aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/3(2015-09-01), 411-426 1618-2510 24:3<411 2015 24 10260