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Safety is a continually significant issue in the construction industry. The Occupation Safety and Health Administration as well as individual construction companies are constantly working on verifying that their selected safety plans have a positive effect on reduction of workplace injuries. Worker safety is a large concern for both the workers and employers in construction and the government also attempts to impose effective regulations concerning minimum safety requirements. There are many different methods for creating and implementing a safety plan, most notably the Construction Industry Institute's (CII) Zero Accidents Techniques (ZAT). This study will attempt to identify a relationship between the level of ZAT implementation and safety performance on industrial construction projects. This research also proposes that focusing efforts on certain ZAT elements over others will show different safety performance results. There are three findings in this study that can be used to assist safety professionals in designing efficient construction safety plans. The first is a significant log-log relationship that is identified between the DEA efficiency scores and Recordable Incident Rate (RIR). There is also a significant difference in safety performance found between the Light Industrial and Heavy Industrial sectors. Lastly, regression is used to show that the pre-construction and worker selection ZAT components can predict a better safety performance. / Master of Science

Spatial analysis of species for which there is limited quantity of data, termed as the data-poor species, has been challenging due to limited information, especially lack of spatially explicit information. However, these species are frequently of high ecological, conservation and management interest. In this study, I used two empirical examples to demonstrate spatial analysis for two kinds of data-poor species. One example was seabird bycatch from the U.S. Atlantic pelagic longline fishery, which focused on rare events/species for which data are generally characterized by a high percentage of zero observations. The other example was endangered white abalone off the California coast, which focused on endangered species whose data are very limited. With the seabird bycatch example, I adopted a spatial filtering technique to incorporate spatial patterns and to improve model performance. The model modified with spatial filters showed superior performance over other candidate models. I also applied the geographically weighted approach to explore spatial nonstationarity in seabird bycatch, i.e., spatial variation in the parameters that describe relationships between biological processes and environmental factors. Estimates of parameters exhibited high spatial variation. With the white abalone example, I demonstrated the spatially explicit hierarchical demographic model and conducted a risk assessment to evaluate the efficacy of hypothetical restoration strategies. The model allowed for the Allee effect (i.e., density-dependent fertilization success) by using spatial explicit density estimates. Restoration efforts directed at larger-size individuals may be more effective in increasing population density than efforts focusing on juveniles. I also explored the spatial nonstationarity in white abalone catch data. I estimated the spatially explicit decline rate and linked the decline rate to environmental factors including water depth, distance to California coast, distance to land, sea surface temperature and chlorophyll concentration. The decline rate showed spatial variation. I did not detect any significant associations between decline rate and these five environmental factors. Through such a study, I am hoping to provide insights on applying or adapting existing methods to model spatial dynamics of data-poor species, and on utilizing information from such analyses to aid in their conservation and management. / Ph. D.

世界衛生組織表示「先前有過自殺行為的人，再度自殺的機率比一般人高」，因此如何針對自殺企圖者提供即時的關懷與介入服務，是世界各國重要的自殺防治策略之一。本研究希望針對曾有過自殺企圖的個案，經由統計模型的配適來找出自殺企圖個案的「自殺危險因子」，區別出再度自殺的高危險個案，以方便將人力及醫療資源投入到最需要被協助的個案上。 本研究的反應變項為「重複自殺企圖次數」，但是由於資料中「零值」的人數相當多，此外也呈現出變異數大於平均數的現象，因此我們採用可以同時處理Zero-inflated及Over-dispersion情況的廣義Zero-inflated卜瓦松迴歸模型 (Generalized Zero-inflated Poisson Regression Model)來進行資料的配適。我們得知重複自殺企圖之高風險因子有「65歲以上」、「曾患有精神疾病」、「不確定是否曾患有精神疾病」及「離婚」之個案，而「治癒」可能性較高的因子為「45~64歲」、「因情感因素自殺」、「已婚」之個案。藉由模型也可以進一步估計自殺企圖個案之再企圖機率，並且對自殺企圖個案進行分層，以進行不同程度的關懷與訪視，藉以提昇關懷的即時性及有效性。 / World Health Organization (WHO) has indicated that suicide attempt rate is much higher among those who have ever had suicide attempts. Hence, how to express concerns and provide timely consultations for suicide reattempters has become one of the key issues in suicide prevention. In this study, we try to identify the risk factors associated with suicide reattempters, and predict high-risk cases so that the limited resources can be distributed effectively. The primary variable of interest is the number of repeated suicide attempt for a suicide attempter after his/her index attempt. However, there are more zeros and greater variability in the data than that would be predicted by a Poisson model. We hence fit the data using a zero-inflated generalized Poisson regression model, a model that is frequently used for modeling over-dispersed count data with too many zeros. We find that the risk factors for repeated suicide attempts are those who are 65 or older, those who are classified as psychiatric disorders and those diagnostically uncertain cases, and those who are divorced. We also find that non-repeaters are more likely among those who are between 45 to 65 of age, married, and having a suicide attempt history due to an emotional reason. Through the use of the model, we can also estimate a subject’s reattempt probability, classify them, and provide them with suitable care and attention accordingly.

As instituições financeiras que pretendem utilizar a IRB (Internal Ratings Based) avançada precisam desenvolver métodos para estimar a componente de risco LGD (Loss Given Default). Desde a década de 1950 são apresentadas propostas para modelagem da PD (Probability of default), em contrapartida, a previsão da LGD somente recebeu maior atenção após a publicação do Acordo Basileia II. A LGD possui ainda uma literatura pequena, se comparada a PD, e não há um método eficiente em termos de acurácia e interpretação como é a regressão logística para a PD. Modelos de regressão para LGD desempenham um papel fundamental na gestão de risco das instituições financeiras. Devido sua importância este trabalho propõe uma metodologia para quantificar a componente de risco LGD. Considerando as características relatadas sobre a distribuição da LGD e na forma flexível que a distribuição beta pode assumir, propomos uma metodologia de estimação da LGD por meio do modelo de regressão beta bimodal inflacionado em zero. Desenvolvemos a distribuição beta bimodal inflacionada em zero, apresentamos algumas propriedades, incluindo momentos, definimos estimadores via máxima verossimilhança e construímos o modelo de regressão para este modelo probabilístico, apresentamos intervalos de confiança assintóticos e teste de hipóteses para este modelo, bem como critérios para seleção de modelos, realizamos um estudo de simulação para avaliar o desempenho dos estimadores de máxima verossimilhança para os parâmetros da distribuição beta bimodal inflacionada em zero. Para comparação com nossa proposta selecionamos os modelos de regressão beta e regressão beta inflacionada, que são abordagens mais usuais, e o algoritmo SVR , devido a significativa superioridade relatada em outros trabalhos. / Financial institutions willing to use the advanced Internal Ratings Based (IRB) need to develop methods to estimate the LGD (Loss Given Default) risk component. Proposals for PD (Probability of default) modeling have been presented since the 1950s, in contrast, LGDs forecast has received more attention only after the publication of the Basel II Accord. LGD also has a small literature, compared to PD, and there is no efficient method in terms of accuracy and interpretation such as logistic regression for PD. Regression models for LGD play a key role in the risk management of financial institutions, due to their importance this work proposes a methodology to quantify the LGD risk component. Considering the characteristics reported on the distribution of LGD and in the flexible form that the beta distribution may assume, we propose a methodology for estimation of LGD using the zero inflated bimodal beta regression model. We developed the zero inflated bimodal beta distribution, presented some properties, including moments, defined estimators via maximum likelihood and constructed the regression model for this probabilistic model, presented asymptotic confidence intervals and hypothesis test for this model, as well as selection criteria of models, we performed a simulation study to evaluate the performance of the maximum likelihood estimators for the parameters of the zero inflated bimodal beta distribution. For comparison with our proposal we selected the beta regression models and inflated beta regression, which are more usual approaches, and the SVR algorithm, due to the significant superiority reported in other studies.

As instituições financeiras que pretendem utilizar a IRB (Internal Ratings Based) avançada precisam desenvolver métodos para estimar a componente de risco LGD (Loss Given Default). Desde a década de 1950 são apresentadas propostas para modelagem da PD (Probability of default), em contrapartida, a previsão da LGD somente recebeu maior atenção após a publicação do Acordo Basileia II. A LGD possui ainda uma literatura pequena, se comparada a PD, e não há um método eficiente em termos de acurácia e interpretação como é a regressão logística para a PD. Modelos de regressão para LGD desempenham um papel fundamental na gestão de risco das instituições financeiras. Devido sua importância este trabalho propõe uma metodologia para quantificar a componente de risco LGD. Considerando as características relatadas sobre a distribuição da LGD e na forma flexível que a distribuição beta pode assumir, propomos uma metodologia de estimação da LGD por meio do modelo de regressão beta bimodal inflacionado em zero. Desenvolvemos a distribuição beta bimodal inflacionada em zero, apresentamos algumas propriedades, incluindo momentos, definimos estimadores via máxima verossimilhança e construímos o modelo de regressão para este modelo probabilístico, apresentamos intervalos de confiança assintóticos e teste de hipóteses para este modelo, bem como critérios para seleção de modelos, realizamos um estudo de simulação para avaliar o desempenho dos estimadores de máxima verossimilhança para os parâmetros da distribuição beta bimodal inflacionada em zero. Para comparação com nossa proposta selecionamos os modelos de regressão beta e regressão beta inflacionada, que são abordagens mais usuais, e o algoritmo SVR , devido a significativa superioridade relatada em outros trabalhos. / Financial institutions willing to use the advanced Internal Ratings Based (IRB) need to develop methods to estimate the LGD (Loss Given Default) risk component. Proposals for PD (Probability of default) modeling have been presented since the 1950s, in contrast, LGDs forecast has received more attention only after the publication of the Basel II Accord. LGD also has a small literature, compared to PD, and there is no efficient method in terms of accuracy and interpretation such as logistic regression for PD. Regression models for LGD play a key role in the risk management of financial institutions, due to their importance this work proposes a methodology to quantify the LGD risk component. Considering the characteristics reported on the distribution of LGD and in the flexible form that the beta distribution may assume, we propose a methodology for estimation of LGD using the zero inflated bimodal beta regression model. We developed the zero inflated bimodal beta distribution, presented some properties, including moments, defined estimators via maximum likelihood and constructed the regression model for this probabilistic model, presented asymptotic confidence intervals and hypothesis test for this model, as well as selection criteria of models, we performed a simulation study to evaluate the performance of the maximum likelihood estimators for the parameters of the zero inflated bimodal beta distribution. For comparison with our proposal we selected the beta regression models and inflated beta regression, which are more usual approaches, and the SVR algorithm, due to the significant superiority reported in other studies.

Made available in DSpace on 2016-06-02T20:06:03Z (GMT). No. of bitstreams: 1 2510.pdf: 1878422 bytes, checksum: 882e21e70271b7a106e3a27a080da004 (MD5) Previous issue date: 2009-04-06 / This work has as central theme the Inflated Modified Power Series Distributions, where the objective is to study its main properties and the applicability in the bayesian context. This class of models includes the generalized Poisson, binomial and negative binomial distributions. These probability distributions are very helpful to models discrete data with inflated values. As particular case the - zero inflated Poisson models (ZIP) is studied, where the main purpose was to verify the effectiveness of it when compared to the Poisson distribution. The same methodology was considered for the negative binomial inflated distribution, but comparing it with the Poisson, negative binomial and ZIP distributions. The Bayes factor and full bayesian significance test were considered for selecting models. / Este trabalho tem como tema central a classe de distribuições série de potências inflacionadas, em que o intuito é estudar suas principais propriedades e a aplicabilidade no contexto bayesiano. Esta classe de modelos engloba as distribuições de Poisson, binomial e binomial negativa simples e as generalizadas e, por isso é muito aplicada na modelagem de dados discretos com valores excessivos. Como caso particular propôs-se explorar a distribuição de Poisson zero inflacionada (ZIP), em que o objetivo principal foi verificar a eficácia de sua modelagem quando comparada à distribuição de Poisson. A mesma metodologia foi considerada para a distribuição binomial negativa inflacionada, mas comparando-a com as distribuições de Poisson, binomial negativa e ZIP. Como critérios formais para seleção de modelos foram considerados o fator de Bayes e o teste de significância completamente bayesiano.

The aim of this thesis is to provide a comprehensive overview of the main approaches to modeling data loaded with redundant zeros. There are three main subclasses of zero modified models (ZMM) described here - zero inflated models (the main focus lies on models of this subclass), zero truncated models and hurdle models. Models of each subclass are defined and then a construction of maximum likelihood estimates of regression coefficients is described. ZMM models are mostly based on Poisson or negative binomial type 2 distribution (NB2). In this work, author has extended the theory to ZIM models generally based on any discrete distributions of exponential type. There is described a construction of MLE of regression coefficients of theese models, too. Just few of present works are interested in ZIM models based on negative binomial type 1 distribution (NB1). This distribution is not of exponential type therefore a common method of MLE construction in ZIM models cannot be used here. In this work provides modification of this method using quasi-likelihood method. There are two simulation studies concluding the work. 1

Master of Science / Department of Statistics / Leigh Murray / The southern root-knot nematode (SRKN) and the weedy perennials, yellow nutsedge (YNS) and purple nutsedge (PNS) are simultaneously occurring pests in the irrigated agricultural soils of southern New Mexico. Previous research has very well characterized SRKN, YNS and PNS as a mutually-beneficial pest complex and has revealed their enhanced population growth and survival when they occur together. The density of nutsedge in a field could be used as a predictor of SRKN juveniles in the soil. In addition to SRKN, which is the most harmful of the plant parasitic nematodes, in southern New Mexico, other species or categories of nematodes could be identified and counted. Some of them are not as damaging to the plant as SRKN, and some of them may be essential for soil health. The nematode species could be grouped into categories according to trophic level (what nematodes eat) and herbivore feeding behavior (how herbivore nematodes eat). Subsequently, three ratios of counts were calculated for trophic level and for feeding behavior level to investigate the soil nematode community structure. These proportions were modeled as functions of the weed hosts YNS and PNS by generalized linear regression models using the logit link function and three probability distributions: the Binomial, Zero Inflated Binomial (ZIB) and Binomial Hurdle (BH). The latter two were used to account for potential high proportions of zeros in the data. The SAS NLMIXED procedure was used to fit models for each of the six sampling dates (May, July and September) over the two years of the alfalfa study. General results showed that the Binomial pmf generally provided the best fit, indicating lower zero-inflation than expected. Importance of YNS and PNS predictors varied over time and the different ratios. Specific results illustrate the differences in estimated probabilities between Binomial, ZIB and BH distributions as YNS counts increase for two selected ratios.