Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.

Relationship Between Orofacial Function, Dentofacial Morphology, And Bite Force In Young Subjects.

The aim was to evaluate the relationship between orofacial function, dentofacial morphology, and bite force in young subjects. Three hundred and sixteen subjects were divided according to dentition stage (early, intermediate, and late mixed and permanent dentition). Orofacial function was screened using the Nordic Orofacial Test-Screening (NOT-S). Orthodontic treatment need, bite force, lateral and frontal craniofacial dimensions and presence of sleep bruxism were also assessed. The results were submitted to descriptive statistics, normality and correlation tests, analysis of variance, and multiple linear regression to test the relationship between NOT-S scores and the studied independent variables. The variance of NOT-S scores between groups was not significant. The evaluation of the variables that significantly contributed to NOT-S scores variation showed that age and presence of bruxism related to higher NOT-S total scores, while the increase in overbite measurement and presence of closed lip posture related to lower scores. Bite force did not show a significant relationship with scores of orofacial dysfunction. No significant correlations between craniofacial dimensions and NOT-S scores were observed. Age and sleep bruxism were related to higher NOT-S scores, while the increase in overbite measurement and closed lip posture contributed to lower scores of orofacial dysfunction.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Influência da idade no comportamento da frequência cardíaca na transição repouso-exercício: uma análise por deltas e regressão linear

BACKGROUND: Changes in heart rate during rest-exercise transition can be characterized by the application of mathematical calculations, such as deltas 0-10 and 0-30 seconds to infer on the parasympathetic nervous system and linear regression and delta applied to data range from 60 to 240 seconds to infer on the sympathetic nervous system. The objective of this study was to test the hypothesis that young and middle-aged subjects have different heart rate responses in exercise of moderate and intense intensity, with different mathematical calculations. METHODS: Seven middle-aged men and ten young men apparently healthy were subject to constant load tests (intense and moderate) in cycle ergometer. The heart rate data were submitted to analysis of deltas (0-10, 0-30 and 60-240 seconds) and simple linear regression (60-240 seconds). The parameters obtained from simple linear regression analysis were: intercept and slope angle. We used the Shapiro-Wilk test to check the distribution of data and the t test for unpaired comparisons between groups. The level of statistical significance was 5%. RESULTS: The value of the intercept and delta 0-10 seconds was lower in middle age in two loads tested and the inclination angle was lower in moderate exercise in middle age. CONCLUSION: The young subjects present greater magnitude of vagal withdrawal in the initial stage of the HR response during constant load exercise and higher speed of adjustment of sympathetic response in moderate exercise.

Estratégias para individualização de resultados em testes de desempenho

Universidade Estadual de Campinas. Faculdade de Educação Física

Fatores associados à capacidade para o trabalho de trabalhadores do Setor Elétrico

O Setor Elétrico passou por recente processo de reestruturação produtiva com reflexos nas condições e organização do trabalho, podendo comprometer a capacidade para o trabalho. Este estudo objetivou avaliar fatores associados à capacidade para o trabalho junto a 475 trabalhadores de uma empresa do Setor Elétrico no Estado de São Paulo, Brasil. Neste estudo transversal foi realizada análise descritiva e análise de regressão linear múltipla. A média do Índice de Capacidade para o Trabalho (ICT) foi de 41,8 pontos (escala de 7,0 a 49,0 pontos). A análise múltipla mostrou que os fatores que melhor explicaram a variabilidade do ICT foram estresse no trabalho (p < 0,001) e saúde física (p < 0,001 em todas as dimensões). Em outra análise, excluídas as dimensões da saúde, as variáveis associadas ao ICT foram estresse no trabalho (p < 0,001), local de trabalho (p = 0,022), prática de atividade física (p = 0,001), consumo de álcool (p = 0,012) e índice de massa corporal (p < 0,001). Os resultados identificaram aspectos a serem considerados no desenvolvimento de medidas visando a preservação da capacidade para o trabalho, com ênfase no controle do estresse no trabalho e na promoção da saúde.

Fatores relacionados à ocorrência de leishmaniose tegumentar no Vale do Ribeira

INTRODUÇÃO: este estudo teve como objetivo identificar fatores ambientais e sociais determinantes na incidência da leishmaniose tegumentar americana no Vale do Ribeira no período de 1998 a 2006. MÉTODOS: foram utilizados dados secundários de domínio público dos 23 municípios que integram a região. O intervalo de tempo foi dividido em três períodos, pelas características gráficas dos coeficientes de incidência, os quais foram submetidos à análise por regressão linear múltipla. RESULTADOS: para o período de 1998 a 2000, as variáveis correlacionadas com a LTA foram índice de desenvolvimento humano médio (p = 0,007), renda per capita (p =0,390) e grau de urbanização (p = 0,079). No período de 2001 a 2003 e 2004 a 2006 as variáveis correlacionadas com LTA foram: a existência de flebotomíneos (p = 0,000 e p = 0,001) e a população urbana média (p = 0,007 e p = 0,001). CONCLUSÕES: esses dados demonstram a tendência de pauperização e urbanização da doença.

Influência da renda sobre as despesas com alimentação fora do domicílio, no Brasil, 2002-2003

Objetivou-se descrever e avaliar a influência da renda sobre a participação da alimentação fora do domicílio no Brasil. Utilizaram-se dados coletados pela Pesquisa de Orçamentos Familiares realizada em 2002/2003 (POF 2002/2003), pelo Instituto Brasileiro de Geografia e Estatística. Analisaram-se os registros dos gastos com aquisições de alimentos e bebidas consumidos fora do domicílio. A associação entre a participação da alimentação fora do domicílio e a renda, ajustada para atributos sócio-demográficos, foi estudada por meio de modelos de regressão utilizados para estimação de coeficientes de elasticidade-renda. A alimentação fora do domicílio representou 21% do total dos gastos com alimentação; destaque-se que o incremento de 10% na renda aumentaria em 3% a participação da alimentação fora do domicílio. O efeito da renda sobre a participação da alimentação fora, ainda que sempre positivo, diminui conforme elevação da renda, sendo alto nos domicílios com renda inferior a R$68,70 per capita/mês. Há influência da renda nos gastos com alimentação fora do domicílio, assim a evolução favorável da renda resultará em aumento dessa forma de se alimentar.

Relationships of blood lead to calcium, iron, and vitamin C intakes in Brazilian pregnant women

Background & aims. This study aimed to determine the relationship between blood lead concentrations and calcium, iron and vitamin C dietary intakes of pregnant women. Methods. Included in the study were 55 women admitted to a hospital, for delivery, from June to August 2002. A food frequency questionnaire was applied to determine calcium, iron and vitamin C intakes, and a general questionnaire to obtain data on demographic-socioeconomic condition, obstetric history, smoking habit, and alcohol intake. Blood lead and haemoglobin were determined, respectively, by atomic absorption spectrometry and by the haemoglobinometer HemoCue®. Multiple linear regression models were used to determine the relationship between blood lead and calcium, iron and vitamin C intakes, and haemoglobin levels, controlling for confounders. Results. The final model of the regression analysis detected an inverse relationship between blood lead and age of the women (p=0.011), haemoglobin (p=0.001), vitamin C (p=0.012), and calcium intake (p<0.001) (R2=0.952). One hundred percent, 98.2% and 43.6% of the women were below the adequate intake (AI) for calcium, and below the recommended dietary allowances (RDA) for iron, and vitamin C, respectively. Conclusion. Despite the small sample size, the results of this study suggest that maternal age, haemoglobin, vitamin C intake, and calcium intake may interfere with blood concentrations of lead

Espacialização da temperatura do ar anual no estado de Alagoas com diferentes modelos digitais de elevação e resoluções espaciais

The aim of this study was to establish a digital elevation model and its horizontal resolution to interpolate the annual air temperature for the Alagoas State by means of multiple linear regression models. A multiple linear regression model was adjusted to series (11 to 34 years) of annual air temperatures obtained from 28 weather stations in the states of Alagoas, Bahia, Pernambuco and Sergipe, in the Northeast of Brazil, in function of latitude, longitude and altitude. The elevation models SRTM and GTOPO30 were used in the analysis, with original resolutions of 90 and 900 m, respectively. The SRTM was resampled for horizontal resolutions of 125, 250, 500, 750 and 900 m. For spatializing the annual mean air temperature for the state of Alagoas, a multiple linear regression model was used for each elevation and spatial resolution on a grid of the latitude and longitude. In Alagoas, estimates based on SRTM data resulted in a standard error of estimate (0.57 degrees C) and dispersion (r(2) = 0.62) lower than those obtained from GTOPO30 (0.93 degrees C and 0.20). In terms of SRTM resolutions, no significant differences were observed between the standard error (0.55 degrees C; 750 m - 0.58 degrees C; 250m) and dispersion (0.60; 500 m - 0.65; 750 m) estimates. The spatialization of annual air temperature in Alagoas, via multiple regression models applied to SRTM data showed higher concordance than that obtained with the GTOPO30, independent of the spatial resolution.

THE MONTHLY RAINFALL IN THE RIO DE JANEIRO STATE, BRAZIL: SEASONALITY AND TREND

The objective of this work was to carry a descriptive analysis in the monthly precipitation of rainfall stations from Rio de Janeiro State, Brazil, using data of position and dispersion and graphical analyses, and to verify the presence of seasonality and trend in these data, with a study about the application of models of time series. The descriptive statistics was to characterize the general behavior of the series in three stations selected which present consistent historical series. The methodology of analysis of variance in randomized blocks and the determination of models of multiple linear regression, considering years and months as predictors variables, disclosed the presence of seasonality, what allowed to infer on the occurrence of repetitive natural phenomena throughout the time and absence of trend in the data. It was applied the methodology of multiple linear regression to removal the seasonality of these time series. The original data had been deducted from the estimates made by the adjusted model and the analysis of variance in randomized blocks for the residues of regression was preceded again. With the results obtained it was possible to conclude that the monthly rainfall present seasonality and they don`t present trend, the analysis of multiple regression was efficient in the removal of the seasonality, and the rainfall can be studied by means of time series.

High-breakdown linear discriminant analysis

The classification rules of linear discriminant analysis are defined by the true mean vectors and the common covariance matrix of the populations from which the data come. Because these true parameters are generally unknown, they are commonly estimated by the sample mean vector and covariance matrix of the data in a training sample randomly drawn from each population. However, these sample statistics are notoriously susceptible to contamination by outliers, a problem compounded by the fact that the outliers may be invisible to conventional diagnostics. High-breakdown estimation is a procedure designed to remove this cause for concern by producing estimates that are immune to serious distortion by a minority of outliers, regardless of their severity. In this article we motivate and develop a high-breakdown criterion for linear discriminant analysis and give an algorithm for its implementation. The procedure is intended to supplement rather than replace the usual sample-moment methodology of discriminant analysis either by providing indications that the dataset is not seriously affected by outliers (supporting the usual analysis) or by identifying apparently aberrant points and giving resistant estimators that are not affected by them.

The influence of lean mass in trabecular and cortical bone in juvenile onset systemic lupus erythematosus

The aim of this study was to evaluate risk factors for low bone mineral density (BMD) and vertebral fractures, in juvenile systemic lupus (JSLE). Thirty-one consecutive patients with JSLE were compared with 31 gender- and age-matched healthy controls. BNID and body composition from all participants were measured using dual-energy X-ray absorptiometry. Vertebral fractures were defined as a reduction of >= 20% of the vertebral height for all patients. Lumbar spine and total femur BMD was significantly decreased in patients compared with controls (P = 0.021 and P = 0.023, respectively). A high frequency of vertebral fractures (22.58%) was found in patients with JSLE. Analysis of body composition revealed lower lean mass (P = 0.033) and higher fat mass percentage (P = 0.003) in patients than in controls. Interestingly, multiple linear regression using BMD as a dependent variable showed a significant association with lean mass in lumbar spine (R(2) = 0.262; P = 0.004) and total femur (R(2) = 0.419, P = 0.0001), whereas no association was observed with menarche age, SLE Disease Activity Index, Systemic Lupus International Collaborating Clinics/American College of Rheumatology, and glucocorticoid. This study indicates that low BMD and vertebral fractures are common in JSLE, and the former is associated with low lean mass, suggesting that muscle rehabilitation may be an additional target for bone therapeutic approach.