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- Creators: Cham, Hei Ning
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
The coastal fishing community of Barrington, Southwest Nova Scotia (SWNS), has depended on the resilience of ocean ecosystems and resource-based economic activities for centuries. But while many coastal fisheries have developed unique ways to govern their resources, global environmental and economic change presents new challenges. In this study, I examine the multi-species fishery of Barrington. My objective was to understand what makes the fishery and its governance system robust to economic and ecological change, what makes fishing households vulnerable, and how household vulnerability and system level robustness interact. I addressed these these questions by focusing on action arenas, their contexts, interactions and outcomes. I used a combination of case comparisons, ethnography, surveys, quantitative and qualitative analysis to understand what influences action arenas in Barrington, Southwest Nova Scotia (SWNS). I found that robustness of the fishery at the system level depended on the strength of feedback between the operational level, where resource users interact with the resource, and the collective-choice level, where agents develop rules to influence fishing behavior. Weak feedback in Barrington has precipitated governance mismatches. At the household level, accounts from harvesters, buyers and experts suggested that decision-making arenas lacked procedural justice. Households preferred individual strategies to acquire access to and exploit fisheries resources. But the transferability of quota and licenses has created divisions between haves and have-nots. Those who have lost their traditional access to other species, such as cod, halibut, and haddock, have become highly dependent on lobster. Based on regressions and multi-criteria decision analysis, I found that new entrants in the lobster fishery needed to maintain high effort and catches to service their debts. But harvesters who did not enter the race for higher catches were most sensitive to low demand and low prices for lobster. This study demonstrates the importance of combining multiple methods and theoretical approaches to avoid tunnel vision in fisheries policy.
ContributorsBarnett, Allain J. D (Author) / Anderies, John M (Thesis advisor) / Abbott, Joshua K (Committee member) / Bolin, Bob (Committee member) / Eakin, Hallie (Committee member) / Arizona State University (Publisher)
Created2014
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