Controlled Morris method: A new factor screening approach empowered by a distribution-free sequential multiple testing procedure

Woods, 2016, Design of experiments for screening, 1 Dunson, 2018, Statistics in the big data era: failures of the machine, Stat Prob Lett, 136, 4, 10.1016/j.spl.2018.02.028 Sanchez, 2006, So many factors, so little time ... simulation experiments in the frequency domain, Int J Prod Econ, 103, 149, 10.1016/j.ijpe.2005.06.007 Schruben, 1987, An experimental procedure for simulation response surface model identification, Commun ACM, 30, 716, 10.1145/27651.27656 Wu, 2009 Morris, 2006, Input screening: finding the important model inputs on a budget, Reliab Eng Syst Saf, 91, 1252, 10.1016/j.ress.2005.11.022 Phoa, 2016, Optimizing two-level supersaturated designs using swarm intelligence techniques, Technometrics, 58, 43, 10.1080/00401706.2014.981346 Xing, 2013, Simulation screening experiments using LASSO-optimal supersaturated design and analysis: a maritime operations application, 497 Kleijnen, 2015 Shi, 2019, An efficient morris method-based framework for simulation factor screening, INFORMS J Comput, inpress Boukouvalas, 2014, An efficient screening method for computer experiments, Technometrics, 56, 422, 10.1080/00401706.2013.866599 Kleijnen, 2005, State-of-the-art review: a user’s guide to the brave new world of designing simulation experiments, INFORMS J Comput, 17, 263, 10.1287/ijoc.1050.0136 Moon, 2012, Two-stage sensitivity-based group screening in computer experiments, Technometrics, 54, 376, 10.1080/00401706.2012.725994 Wan, 2006, Controlled sequential bifurcation: a new factor-screening method for discrete-event simulation, Oper Res, 54, 743, 10.1287/opre.1060.0311 Ankenman, 2015, Screening for dispersion effects by sequential bifurcation, ACM Trans Model Comput Simul, 25, 2/1, 10.1145/2651364 Bettonvil, 1997, Searching for important factors in simulation models with many factors: sequential bifurcation, Eur J Oper Res, 96, 180, 10.1016/S0377-2217(96)00156-7 Shi, 2014, Factor screening for simulation with multiple responses: sequential bifurcation, Eur J Oper Res, 237, 136, 10.1016/j.ejor.2014.02.021 Wan, 2010, Improving the efficiency and efficacy of controlled sequential bifurcation for simulation factor screening, INFORMS J Comput, 22, 482, 10.1287/ijoc.1090.0366 Morris, 1991, Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161, 10.1080/00401706.1991.10484804 Brewer, 2017, A sensitivity analysis of key natural factors in the modeled global acetone budget, J Geophys Res, 122, 2043, 10.1002/2016JD025935 Matthias, 2018, Conditioning a hydrologic model using patterns of remotely sensed land surface temperature, Water Resour Res, 54, 2976, 10.1002/2017WR021346 Lamoureux, 2014, A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators, Reliab Eng Syst Saf, 130, 12, 10.1016/j.ress.2014.03.007 Deman, 2015, Sensitivity analysis of groundwater lifetime expectancy to hydro-dispersive parameters: the case of ANDRA meuse/haute-Marne site, Reliab Eng Syst Saf, 134, 276, 10.1016/j.ress.2014.08.005 Xiao, 2016, A new effective screening design for structural sensitivity analysis of failure probability with the epistemic uncertainty, Reliab Eng Syst Saf, 156, 1, 10.1016/j.ress.2016.07.014 Wald, 1992, 256 De, 2012, Step-up and step-down methods for testing multiple hypotheses in sequential experiments, J Stat Plan Infer, 142, 2059, 10.1016/j.jspi.2012.02.005 De, 2012, Sequential bonferroni methods for multiple hypothesis testing with strong control of family-wise error rates i and II, Seq Anal, 31, 238, 10.1080/07474946.2012.665730 Bartroff, 2014, Sequential tests of multiple hypotheses controlling type i and II familywise error rates, J Stat Plan Inference, 153, 100, 10.1016/j.jspi.2014.05.010 Antoniak, 1968, A distribution-free sequential probability-ratio test for multiple-resolution-element radars, IEEE Trans Inf Theory, 14, 822, 10.1109/TIT.1968.1054227 Yu, 2004, A non-parametric sequential rank-sum probability ratio test method for binary hypothesis testing, Signal Process, 84, 1267, 10.1016/j.sigpro.2004.04.008 Campolongo, 1999, The use of graph theory in the sensitivity analysis of the model output: a second order screening method, Reliab Eng Syst Saf, 64, 1, 10.1016/S0951-8320(98)00008-8 Cropp, 2002, The new morris method: an efficient second-order screening method, Reliab Eng Syst Saf, 78, 77, 10.1016/S0951-8320(02)00109-6 Campolongo, 2007, An effective screening design for sensitivity analysis of large models, Environ Model Softw, 22, 1509, 10.1016/j.envsoft.2006.10.004 Fédou, 2015, Extending morris method: identification of the interaction graph using cycle-equitable designs, J Stat Comput Simul, 85, 1398, 10.1080/00949655.2014.997235 Ge, 2015, Combining screening and metamodel-based methods: an efficient sequential approach for the sensitivity analysis of model outputs, Reliab Eng Syst Saf, 134, 334, 10.1016/j.ress.2014.08.009 Ge, 2017, Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs, Reliab Eng Syst Saf, 162, 28, 10.1016/j.ress.2017.01.010 Dykstra, 2005, Kullback–Leibler information, 1 Wang, 2014, Sequential procedures for multiple responses factor screening, 745 Han, 2008, Sequential kernel density approximation and its application to real-time visual tracking, IEEE Trans Pattern Anal Mach Intell, 30, 1186, 10.1109/TPAMI.2007.70771 Zhang, 2014, A Gaussian kernel-based approach for modeling vehicle headway distributions, Transp Sci, 48, 206, 10.1287/trsc.1120.0451 Parpas, 2015, Importance sampling in stochastic programming: a markov chain monte carlo approach, INFORMS J Comput, 27, 358, 10.1287/ijoc.2014.0630 Kristan, 2010, Online kernel density estimation for interactive learning, Image Vis Comput, 28, 1106, 10.1016/j.imavis.2009.09.010 Kristan, 2011, Multivariate online kernel density estimation with gaussian kernels, Pattern Recognit, 44, 2630, 10.1016/j.patcog.2011.03.019 De, 2015, Sequential tests controlling generalized familywise error rates, Stat Methodol, 23, 88, 10.1016/j.stamet.2014.10.001 Jennen-Steinmetz, 1988, A unifying approach to nonparametric regression estimation, J Am Stat Assoc, 83, 1084, 10.1080/01621459.1988.10478705 Gasser, 1990, The choice of weights in kernel regression estimation, Biometrika, 77, 377, 10.1093/biomet/77.2.377 Härdle, 1990 López-Rubio, 2008, Soft clustering for nonparametric probability density function estimation, Pattern Recognit Lett, 29, 2085, 10.1016/j.patrec.2008.07.010 Deng, 2008, FRSDE: fast reduced set density estimator using minimal enclosing ball approximation, Pattern Recognit, 41, 1363, 10.1016/j.patcog.2007.09.013 Efron, 1993, 57 Seaman, 1999, Effects of sample size on kernel home range estimates, J Wildl Manag, 739, 10.2307/3802664 Pujol, 2009, Simplex-based screening designs for estimating metamodels, Reliab Eng Syst Saf, 94, 1156, 10.1016/j.ress.2008.08.002 Shi, 2017, Controlled Morris method: a new distribution-free sequential testing procedure for simulation 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