Matching Items (3)
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- All Subjects: Analysis of variance
- All Subjects: manufacturing
- Genre: Academic theses
- Creators: Broatch, Jennifer
- Creators: Enders, Stephen G
- Creators: Georgeou, Trian
- Status: Published
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
In the industry of manufacturing, each gas turbine engine component begins in a raw state such as bar stock and is routed through manufacturing processes to define its final form before being installed on the engine. What is the follow-up to this part? What happens when over time and usage it wears? Several factors have created a section of the manufacturing industry known as aftermarket to support the customer in their need for restoration and repair of their original product. Once a product has reached a wear factor or cycle limit that cannot be ignored, one of the options is to have it repaired to maintain use of the core. This research investigated the study into the creation and application of repair development methodology that can be utilized by current and new manufacturing engineers of the world. Those who have been in this field for some time will find the process thought provoking while the engineering students can develop a foundation of thinking to prepare for the common engineering problems they will be tasked to resolve. The examples, figures and tables are true issues of the industry though the data will have been changed due to proprietary factors. The results of the study reveals, under most scenarios, a solid process can be followed to proceed with the best options for repair based on the initial discrepancy. However, this methodology will not be a "catch-all" process but a guidance that will develop the proper thinking in evaluation of the repair options and the possible failure modes of each choice. As with any continuous improvement tool, further research is needed to test the applicability of this process in other fields.
ContributorsMoedano, Jesus A (Author) / Lewis, Sharon L (Thesis advisor) / Meitz, Robert (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2013
Description
The Partition of Variance (POV) method is a simplistic way to identify large sources of variation in manufacturing systems. This method identifies the variance by estimating the variance of the means (between variance) and the means of the variance (within variance). The project shows that the method correctly identifies the variance source when compared to the ANOVA method. Although the variance estimators deteriorate when varying degrees of non-normality is introduced through simulation; however, the POV method is shown to be a more stable measure of variance in the aggregate. The POV method also provides non-negative, stable estimates for interaction when compared to the ANOVA method. The POV method is shown to be more stable, particularly in low sample size situations. Based on these findings, it is suggested that the POV is not a replacement for more complex analysis methods, but rather, a supplement to them. POV is ideal for preliminary analysis due to the ease of implementation, the simplicity of interpretation, and the lack of dependency on statistical analysis packages or statistical knowledge.
ContributorsLittle, David John (Author) / Borror, Connie (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015