926 resultados para C30 - General-Sectional Models
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We want to shed some light on the development of person mobility by analysing the repeated cross-sectional data of the four National Travel Surveys (NTS) that were conducted in Germany since the mid seventies. The above mentioned driving forces operate on different levels of the system that generates the spatial behaviour we observe: Travel demand is derived from the needs and desires of individuals to participate in spatially separated activities. Individuals organise their lives in an interactive process within the context they live in, using given infrastructure. Essential determinants of their demand are the individual's socio-demographic characteristics, but also the opportunities and constraints defined by the household and the environment are relevant for the behaviour which ultimately can be realised. In order to fully capture the context which determines individual behaviour, the (nested) hierarchy of persons within households within spatial settings has to be considered. The data we will use for our analysis contains information on these three levels. With the analysis of this micro-data we attempt to improve our understanding of the afore subsumed macro developments. In addition we will investigate the prediction power of a few classic sociodemographic variables for the daily travel distance of individuals in the four NTS data sets, with a focus on the evolution of this predictive power. The additional task to correctly measure distances travelled by means of the NTS is threatened by the fact that although these surveys measure the same variables, different sampling designs and data collection procedures were used. So the aim of the analysis is also to detect variables whose control corrects for the known measurement error, as a prerequisite to apply appropriate models in order to better understand the development of individual travel behaviour in a multilevel context. This task is complicated by the fact that variables that inform on survey procedures and outcomes are only provided with the data set for 2002 (see Infas and DIW Berlin, 2003).
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Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.
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The objective of the EU funded integrated project "ACuteTox" is to develop a strategy in which general cytotoxicity, together with organ-specific endpoints and biokinetic features, are taken into consideration in the in vitro prediction of oral acute systemic toxicity. With regard to the nervous system, the effects of 23 reference chemicals were tested with approximately 50 endpoints, using a neuronal cell line, primary neuronal cell cultures, brain slices and aggregated brain cell cultures. Comparison of the in vitro neurotoxicity data with general cytotoxicity data generated in a non-neuronal cell line and with in vivo data such as acute human lethal blood concentration, revealed that GABA(A) receptor function, acetylcholine esterase activity, cell membrane potential, glucose uptake, total RNA expression and altered gene expression of NF-H, GFAP, MBP, HSP32 and caspase-3 were the best endpoints to use for further testing with 36 additional chemicals. The results of the second analysis showed that no single neuronal endpoint could give a perfect improvement in the in vitro-in vivo correlation, indicating that several specific endpoints need to be analysed and combined with biokinetic data to obtain the best correlation with in vivo acute toxicity.
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OBJECTIVES: Alcohol use is associated with self-reported health status. However, little is known about the concurrent association between alcohol screening scores and patient perception of health. We evaluated this association in a sample of primarily older male veterans.METHODS: This secondary, cross-sectional analysis included male general medicine outpatients from 7 VA medical centers who returned mailed questionnaires. Screening scores from the Alcohol Use Disorders Identification Test Consumption (AUDIT-C) questionnaire were divided into 6 categories (0, 13, 45, 67, 89, and 1012). Outcomes included scores on the 8 subscales and 2 component scores of the 36-item Short Form Health Survey (SF-36). Unadjusted and adjusted linear regression models were fit to characterize the association between AUDIT-C categories and SF-36 scores. Models were adjusted for demographic characteristics, smoking, and site?both alone and in combination with 14 self-reported comorbid conditions.RESULTS: Male respondents (n = 24,531; mean age = 63.6 years) represented 69% of those surveyed with the SF-36. After adjustment, a quadratic (inverted U-shaped) relationship was demonstrated between AUDIT-C categories and all SF-36 scores such that patients with AUDIT-C scores 45 or 67 reported the highest health status, and patients with AUDIT-C scores 0, 89, and =10 reported the lowest health status.CONCLUSIONS: Across all measures of health status, patients with the most severe alcohol misuse had significantly poorer health status than those who screened positive for alcohol misuse at mild or moderate levels of severity. The relatively good health status reported by patients with mild-moderate alcohol misuse might interfere with clinicians' acceptance and adoption of guidelines recommending that they counsel these patients about their drinking.
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Objectives: To identify factors that correlate with insulin values and to examine its independent associations among adolescents. Methods: A cross-sectional population-based study was conducted among adolescents aged 12-16,9 years old. A multi-stage stratified cluster random sampling method was employed. Anthropometric measurements and nutritional survey were performed, and fasting blood samples for insulin were obtained. Statistics: Multiple lineal regression. Results: 379 adolescents were included. Mean age was 14.08 ± 1.30 years. Factors associated with higher fasting insulin levels were puberty [ 4.55 (95% IC 0.42-8.69)], abdominal obesity [ 6.11 (95% IC 3.93-8.29)] and to be born small for gestational age (SGA) [ 7.45 (95% IC 2.47-12.44)]. It was observed a negative association between the regular intake of olive oil at home and insulin values [ -4.14 (95% IC -7.31- -0.98)]. Conclusions: Abdominal obesity and SGA were factors associated with higher fasting insulin values. In contrast, the regular intake of olive oil at home was an independent protective factor.
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Age and sex have been identified as predictors of outcome in malignant melanoma (MM). This aim of this multicentre, cross-sectional study was to analyse the role of age and sex as explanatory variables for the diagnosis of thin MM. A total of 2430 patients with MM were recruited. Cases of in situ-T1 MM were more frequent than T2-T4 MM (56.26% vs. 43.74%). Breslow thickness increased throughout decades of life (analysis of variance (ANOVA) p < 0.001), with a weak correlation between Breslow thickness and patient's age (r = 0.202, p < 0.001). Breslow thickness was significantly less in women (1.79 vs. 2.38 mm, p = 0.0001). Binary logistic regression showed a significant (p < 0.001) odds ratio for age 0-29 years (1.18), and 30-59 years (1.16), and for women (1.09). Age and sex explained 3.64% of the variation observed in Tis-T1 frequency (R2 = 0.0364). Age and sex appear to explain a low percentage of the variation in the early detection of MM.
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Calculating explicit closed form solutions of Cournot models where firms have private information about their costs is, in general, very cumbersome. Most authors consider therefore linear demands and constant marginal costs. However, within this framework, the nonnegativity constraint on prices (and quantities) has been ignored or not properly dealt with and the correct calculation of all Bayesian Nash equilibria is more complicated than expected. Moreover, multiple symmetric and interior Bayesianf equilibria may exist for an open set of parameters. The reason for this is that linear demand is not really linear, since there is a kink at zero price: the general ''linear'' inverse demand function is P (Q) = max{a - bQ, 0} rather than P (Q) = a - bQ.
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OBJECTIVE: To identify which physician and patient characteristics are associated with physicians' estimation of their patient social status.DESIGN: Cross-sectional ulticentric survey. SETTING: Fourty-seven primary care private offices in Western Switzerland. PARTICIPANTS: Random sample of 2030 patients ≥ 16, who encountered a general practitioner (GP) between September 2010 and February 2011. MAIN MEASURES: PRIMARY OUTCOME: patient social status perceived by GPs, using the MacArthur Scale of Subjective Social Status, ranging from the bottom (0) to the top (10) of the social scale.Secondary outcome: Difference between GP's evaluation and patient's own evaluation of their social status. Potential patient correlates: material and social deprivation using the DiPCare-Q, health status using the EQ-5D, sources of income, and level of education. GP characteristics: opinion regarding patients' deprivation and its influence on health and care. RESULTS: To evaluate patient social status, GPs considered the material, social, and health aspects of deprivation, along with education level, and amount and type of income. GPs declaring a frequent reflexive consideration of their own prejudice towards deprived patients, gave a higher estimation of patients' social status (+1.0, p = 0.002). Choosing a less costly treatment for deprived patients was associated with a lower estimation (-0.7, p = 0.002). GP's evaluation of patient social status was 0.5 point higher than the patient's own estimate (p<0.0001). CONCLUSIONS: GPs can perceive the various dimensions of patient social status, although heterogeneously, according partly to their own characteristics. Compared to patients' own evaluation, GPs overestimate patient social status.
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Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.
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Background: Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks. Results: First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented. Conclusion: In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.
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An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.
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OBJECTIVE: To better understand the structure of the Patient Assessment of Chronic Illness Care (PACIC) instrument. More specifically to test all published validation models, using one single data set and appropriate statistical tools. DESIGN: Validation study using data from cross-sectional survey. PARTICIPANTS: A population-based sample of non-institutionalized adults with diabetes residing in Switzerland (canton of Vaud). MAIN OUTCOME MEASURE: French version of the 20-items PACIC instrument (5-point response scale). We conducted validation analyses using confirmatory factor analysis (CFA). The original five-dimension model and other published models were tested with three types of CFA: based on (i) a Pearson estimator of variance-covariance matrix, (ii) a polychoric correlation matrix and (iii) a likelihood estimation with a multinomial distribution for the manifest variables. All models were assessed using loadings and goodness-of-fit measures. RESULTS: The analytical sample included 406 patients. Mean age was 64.4 years and 59% were men. Median of item responses varied between 1 and 4 (range 1-5), and range of missing values was between 5.7 and 12.3%. Strong floor and ceiling effects were present. Even though loadings of the tested models were relatively high, the only model showing acceptable fit was the 11-item single-dimension model. PACIC was associated with the expected variables of the field. CONCLUSIONS: Our results showed that the model considering 11 items in a single dimension exhibited the best fit for our data. A single score, in complement to the consideration of single-item results, might be used instead of the five dimensions usually described.
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The present thesis comprises two study populations. The first study sample (SS1) consisted of 411 adults examined and interviewed at three annual visits. The second study sample (SS2) consisted of 1720 adults who filled in a mailed questionnaire about secondary otalgia, tinnitus and fullness of ears. In the second phase of the SS2, 100 subjects with otalgia were examined and interviewed by specialist in stomatognathic physiology and otorhinolaryngology. In the third phase, 36 subjects participated in a randomized, controlled and blinded trial of effectiveness of occlusal appliance on secondary otalgia, facial pain, headache and treatment need of temporomandibular disorders (TMD). The standardized prevalence of recurrent secondary otalgia was 6%, tinnitus 15% and fullness of ears 8%. Aural symptoms were more frequent among young than old subjects. They were associated with other, simultaneous aural symptoms, TMD pain, head and neck region pain, and visits to a physician. The subjects with aural symptoms more often had tenderness on palpation of masticatory muscles and clinical signs of temporomandibular joint than the subjects without. 85% of the subjects reporting secondary otalgia had cervical spine or temporomandibular disorder or both. In SS1, the final model of secondary otalgia included active need treatment for TMD, elevated level of stress symptoms, and bruxism. In SS2, the final models of aural symptoms included associated aural symptoms, young age, TMD pain, headache and shoulder ache. Stabilization splint more effectively alleviated secondary otalgia and active treatment need for TMD than a palatal control splint. In patients with aural pain, tinnitus or fullness of ears, it is important to first rule out otologic and nasopharyngeal diseases that may cause the symptoms. If no explanation for aural symptoms is found, temporomandibular and cervical spine disorders should be rouled out to minimize unnecessary visits to a physician.
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OBJECTIVE To compare the health assistance models of Basic Traditional Units (UBS) with the Family Health Strategy (ESF) units for presence and extent of attributes of Primary Health Care (APS), specifically in the care of children. METHOD A cross-sectional study of a quantitative approach with families of children attended by the Public Health Service of Colombo, Paraná. The Primary Care Assessment Tool (PCA-Tool) was applied to parents of 482 children, 235 ESF units and 247 UBS units covering all primary care units of the municipality, between June and July 2012. The results were analyzed according to the PCA-Tool manual. RESULTS ESF units reached a borderline overall score for primary health care standards. However, they fared better in their attributes of Affiliation, Integration of care coordination, Comprehensiveness, Family Centeredness and Accessibility of use, while the attributes of Community Guidance/Orientation, Coordination of Information Systems, Longitudinality and Access attributes were rated as insufficient for APS. UBS units had low scores on all attributes. CONCLUSION The ESF units are closer to the principles of APS (Primary Health Care), but there is need to review actions of child care aimed at the attributes of APS in both care models, corroborating similar studies from other regions of Brazil.