Vorlesungen über die Lehre von den einfachen und mehrfachen bestimmten Integralen /

Preservation photocopy on alkaline paper.

Neighborhoods neighboring neighborhoods: adjacency, relative position and tract-level racial change in the U.S. 2000 to 2010

Thesis (Master's)--University of Washington, 2016-06

Boundary Harnack Principle for Stable-Like Processes

Thesis (Ph.D.)--University of Washington, 2016-06

Violence against young Australian women and association with reproductive events: a cross-sectional analysis of a national population sample

Objective: This study aimed to investigate associations between violence and younger women's reproductive events using Survey 1 (1996) data of the Younger cohort of the Australian Longitudinal Study of Women's Health (ALSWH). Methods: Multinomial regression, using composite variables for both violence and reproductive events, adjusting for socioeconomic variables and weighted for rural and remote areas. Results: 23.8% of 14,784 women aged 18 to 23 years reported violence; 12.6% reported non-partner violence in the previous year; and 11.2% reported ever having had a violent relationship with a partner. Of the latter group, 43% (4.8% overall) also reported violence in the past year. Compared with women reporting no violence, women reporting partner but not recent violence (OR 2.55, 95% Cl 2.10-3.09) or partner and recent violence (OR 3.96, 95% Cl 3.18-4.93) were significantly more likely to have had one or more pregnancies. Conversely, having had a pregnancy (2,561) was associated with an 80% increase in prevalence of any violence and a 230% increase in partner violence. Among women who had a pregnancy, having had a miscarriage or termination was associated with violence. Partner and recent violence is strongly associated with having had a miscarriage, whether alone (OR = 2.85, 95% Cl 1.74-4.66), with a termination (OR = 4.60, 2.26-9.35), or with birth, miscarriage and a termination (OR 4.12, 1.89-9.00). Conclusions and implications: Violence among young women of childbearing age is a factor for which doctors should be vigilant, well-trained and supported to identify and manage effectively.

Disorders of breathing and continence have a stronger association with back pain than obesity and physical activity

Although obesity and physical activity have been argued to predict back pain, these factors are also related to incontinence and breathing difficulties. Breathing and continence mechanisms may interfere with the physiology of spinal control, and may provide a link to back pain. The aim of this study was to establish the association between back pain and disorders of continence and respiration in women. We conducted a cross-sectional analysis of self-report, postal survey data from the Australian Longitudinal Study on Women's Health. We used multinomial logistic regression to model four levels of back pain in relation to both the traditional risk factors of body mass index and activity level, and the potential risk factors of incontinence, breathing difficulties, and allergy. A total of 38 050 women were included from three age-cohorts. When incontinence and breathing difficulties were considered, obesity and physical activity were not consistently associated with back pain. In contrast, odds ratios (OR) for often having back pain were higher for women often having incontinence compared to women without incontinence (OR were 2.5, 2.3 and 2.3 for young, mid-age! and older women, respectively). Similarly, mid-aged and older women had higher odds of having back pain often when they experienced breathing difficulties often compared to women with no breathing problems (OR of 2.0 and 1.9, respectively). Unlike obesity and physical activity, disorders of continence and respiration were strongly related to frequent back pain. This relationship may be explained by physiological limitations of co-ordination of postural, respiratory and continence functions of trunk muscles.

In utero and postnatal maternal smoking and asthma in adolescence

Background: Asthma in early childhood has been associated with maternal smoking during pregnancy and parental smoking soon after birth. However, less is known about these exposures and the development of asthma symptoms in adolescence. Methods: Data were taken from the Mater University Study, of Pregnancy, a large birth cohort study of mothers and children enrolled in Brisbane, Australia, beginning in 1981. Smoking was assessed at 2 stages during pregnancy and at the 6-month and 5-year follow-up visits. Asthma was assessed from maternal reports that were provided when the child was age 14 years. We conducted multivariable multinomial logistic regression analyses to assess the effect of maternal smoking on asthma symptoms. Results: There was a strong sex interaction such that girls whose mothers had smoked heavily (20 or more cigarettes per day) in pregnancy and at the 6-month follow up had increased odds of experiencing asthma symptoms at age 14 (odds ratio = 1.96; 95% confidence interval = 1.25-3.08). The contribution of heavy smoking during pregnancy appeared to be stronger than heavy smoking after the birth. No similar associations were seen for boys. Conclusion: Female adolescents whose mothers smoked heavily during the fetal period and the early months of life have increased risk of asthma symptoms in adolescence. In utero exposure to heavy smoking was found to have a stronger effect than postnatal environmental tobacco exposure.

A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.

Subtypes of Illicit Drug Users: A Latent Class Analysis of Data From an Australian Twin Sample

This article applies methods of latent class analysis (LCA) to data on lifetime illicit drug use in order to determine whether qualitatively distinct classes of illicit drug users can be identified. Self-report data on lifetime illicit drug use (cannabis, stimulants, hallucinogens, sedatives, inhalants, cocaine, opioids and solvents) collected from a sample of 6265 Australian twins (average age 30 years) were analyzed using LCA. Rates of childhood sexual and physical abuse, lifetime alcohol and tobacco dependence, symptoms of illicit drug abuse/dependence and psychiatric comorbidity were compared across classes using multinomial logistic regression. LCA identified a 5-class model: Class 1 (68.5%) had low risks of the use of all drugs except cannabis; Class 2 (17.8%) had moderate risks of the use of all drugs; Class 3 (6.6%) had high rates of cocaine, other stimulant and hallucinogen use but lower risks for the use of sedatives or opioids. Conversely, Class 4 (3.0%) had relatively low risks of cocaine, other stimulant or hallucinogen use but high rates of sedative and opioid use. Finally, Class 5 (4.2%) had uniformly high probabilities for the use of all drugs. Rates of psychiatric comorbidity were highest in the polydrug class although the sedative/opioid class had elevated rates of depression/suicidal behaviors and exposure to childhood abuse. Aggregation of population-level data may obscure important subgroup differences in patterns of illicit drug use and psychiatric comorbidity. Further exploration of a 'self-medicating' subgroup is needed.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

DETERMINANTE DE COMPOSIÇÃO DE CONSELHO DE ADMINISTRAÇÃO EM PEQUENAS E MÉDIAS EMPRESAS DE SOCIEDADE ANÔNIMA DE CAPITAL FECHADO DA REGIÃO DO ABC

O principal objetivo deste estudo é analisar os determinantes de composição de Conselho de Administração em pequenas e médias empresas de sociedade anônima de capital fechado da região do ABC paulista. A razão de iniciar este trabalho, surgiu devido à constatação de que no Brasil existem poucos artigos e dissertações que tratam de composição de Conselho e os elaborados no exterior se centralizaram na composição em termos de tamanho e tipos de diretores, de desempenho financeiro, de independência etc., mas nenhum deles focou nas determinantes de composição de pequena e média empresa. Portanto, baseando-se nas observações acima, se efetuou pesquisa de campo para responder à seguinte problemática Que determinante(s) estabelece(m) a composição de Conselho de Administração de Pequenas e Médias Empresas de Sociedade Anônima de Empresas da Região do ABC paulista? Partindo dessa problemática, se estabeleceu as seguintes hipóteses: se o poder e a influência do CEO/Presidente do Conselho em empresas de pequeno e médio porte são grandes, então existem baixas possibilidades de ter Conselheiros Externos; se houver segregação de cargos entre o CEO e o Presidente do Conselho e o CEO estiver interessado em preservar a sua atuação, então há probabilidade de escolher Conselheiros Internos; se houver segregação de cargos entre o CEO e o Presidente do Conselho e o CEO estiver interessado na eficiência, orientação e na necessidade de recursos externos, então há probabilidade de escolher Conselheiros Externos; se a empresa está no Ciclo de Vida Expansão e Maturidade- , então há possibilidades de adotarem Conselheiros Externos.(AU)

Evaluating service quality and customer satisfaction evaluation in an emerging markets

Developing economies offer tremendous potential for future growth and organizations appreciating these consumers’ requirements stand to reap considerable returns. However, compared with more developed economies published consumer studies are few. In particular, there is a dearth of service quality research and hardly any from Africa. Furthermore, the little available research tends to apply Western methodologies, which may not be entirely appropriate. This research investigates East African consumer perceptions of retail banking using an approach that takes account of the research context. Qualitative research was undertaken to define the relevant service attributes. Performance along these was then investigated through a survey with over 2000 respondents. Principal component analysis identifies 13 core service dimensions and multinomial logistic regression reveals which are the key drivers of customer satisfaction. Comparison of the results with studies from other regions confirms that established standardized research instruments are likely to miss or under-represent service attributes important in developing countries.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.