Simulation Factor Screen in Binary Response Models

To eliminate unimportant factors so that the remaining important factors can be further studied in later experimentation, screening experiments (which may be physical or simulation based) are typically performed. This thesis proposes a hybrid statistical procedure for efficient factor screening via simulation experiments. The hybrid procedure is particularly developed for cases where the system response is binary, as opposed to continuous; such a factor-screening procedure does not exist yet in the literature.;The proposed hybrid procedure integrates two screening methods: the sequential factorial design with multivariate sequential test (SFD-MT), which is newly developed in this work, and the modified controlled sequential bifurcation (CSB), which is adapted from the existing CSB method. At the beginning of the procedure, a pre-screening process is conducted to obtain the preliminary estimates of factor effects, and to determine whether SFD-MT or modified CSB will be used for factor screening. Then the selected screening method (either SFD-MT or modified CSB) are performed to identify the important factors based on simulation experiments. In both SFD-MT and CSB, the type I and type II errors are approximately controlled through appropriate hypothesis tests. The efficiency of the hybrid procedure over the CSB in the literature is demonstrated via empirical experiments

A hybrid Data Quality Indicator and statistical method for improving uncertainty analysis in LCA of a small off-grid wind turbine

In Life Cycle Assessment (LCA) uncertainty analysis has been recommended when choosing sustainable products. Both Data Quality Indicator and statistical methods are used to estimate data uncertainties in LCA. Neither of these alone is however adequate enough to address the challenges in LCA of a complex system due to data scarcity and large quantity of material types. This paper applies a hybrid stochastic method, combining the statistical and Data Quality Indicator methods by using a pre-screening process based on Monte Carlo rank-order correlation sensitivity analysis, to improve the uncertainty estimate in wind turbine LCA with data limitations. In the presented case study which performed the stochastic estimation of CO2 emissions, similar results from the hybrid method were observed compared to the pure Data Quality Indicator method. Summarily, the presented hybrid method can be used as a possible alternative for evaluating deterministic LCA results like CO2 emissions, when results that are more reliable are desired with limited availability of data