Matching Items (3)
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- All Subjects: Robustness
- Creators: Cham, Hei Ning
- Creators: Anderies, John M.
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
For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the exogenous indicators, which have not been investigated in previous literature. Results showed that the constrained product indicator and LMS approaches yielded biased estimates of the interaction effect when the exogenous indicators were highly nonnormal. When the violation of nonnormality was not severe (symmetric with excess kurtosis < 1), the LMS approach with ML estimation yielded the most precise latent interaction effect estimates. The LMS approach with ML estimation also had the highest statistical power among the three approaches, given that the actual Type-I error rates of the Wald and likelihood ratio test of interaction effect were acceptable. In highly nonnormal conditions, only the GAPI approach with ML estimation yielded unbiased latent interaction effect estimates, with an acceptable actual Type-I error rate of both the Wald test and likelihood ratio test of interaction effect. No support for the use of the Satorra-Bentler or Yuan-Bentler ML corrections was found across all three methods.
ContributorsCham, Hei Ning (Author) / West, Stephen G. (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2010
Description
Social-ecological systems (SES) are replete with hard and soft human-made components (or infrastructures) that are consciously-designed to perform specific functions valued by humans. How these infrastructures mediate human-environment interactions is thus a key determinant of many sustainability problems in present-day SES. This dissertation examines the question of how some of the designed aspects of physical and social infrastructures influence the robustness of SES under global change. Due to the fragility of rural livelihood systems, locally-managed common-pool resource systems that depend on infrastructure, such as irrigated agriculture and community forestry, are of particular importance to address this sustainability question. This dissertation presents three studies that explored the robustness of communal irrigation and forestry systems to economic or environmental shocks. The first study examined how the design of irrigation infrastructure affects the robustness of system performance to an economic shock. Using a stylized dynamic model of an irrigation system as a testing ground, this study shows that changes in infrastructure design can induce fundamental changes in qualitative system behavior (i.e., regime shifts) as well as altered robustness characteristics. The second study explored how connectedness among social units (a kind of social infrastructure) influenced the post-failure transformations of large-N forest commons under economic globalization. Using inferential statistics, the second study argues that some attributes of the social connectedness that helped system robustness in the past made the system more vulnerable to undesirable transformations in the current era. The third study explored the question of how to guide adaptive management of SES for more robustness under uncertainty. This study used an existing laboratory behavioral experiment in which human-subjects tackle a decision problem on collective management of an irrigation system under environmental uncertainty. The contents of group communication and the decisions of individuals were analyzed to understand how configurations of learning-by-doing and other adaptability-related conditions may be causally linked to robustness under environmental uncertainty. The results show that robust systems are characterized by two conditions: active learning-by-doing through outer-loop processes, i.e., frequent updating of shared assumptions or goals that underlie specific group strategies, and frequent monitoring and reflection of past outcomes.
ContributorsYu, Jae Hoon David (Author) / Anderies, John M. (Thesis advisor) / Janssen, Marco A. (Committee member) / Muneepeerakul, Rachata (Committee member) / Arizona State University (Publisher)
Created2015