Search results for "Logistic regression"
showing 10 items of 835 documents
Predictors of time famine among Finnish employees - Work, family or leisure?
2009
The recent survey data indicates that the time famine is a common experience among employees, while the data of time use indicates increased leisure time. Similarly, there are different views on the causes of time famine. Firstly, in working life research time famine is usually explained by increasing requirements of work life. Secondly, in gender studies time famine is considered to be a product of family obligations. Thirdly, some authors interpret time famine as a phenomenon relating to the intensification of leisure. The aim of the study was to examine the extent and causes of time famine among Finnish employees. The analysis was based on the Finnish Use of Time data (1999–2000) and foc…
What keeps young adults in permanent poverty? A comparative analysis using ECHP
2009
Abstract Previous studies suggest that there are strong differences in the rates of youth poverty across European countries. Rather surprisingly, it is found to be high in Scandinavian countries, and relatively speaking, lower in Mediterranean and Anglo-Saxon countries. This somewhat unexpected finding prompts the question whether the incidence of poverty is an appropriate measure of youth disadvantage. Instead of considering poverty rates we consider the length of recorded poverty spells, taking into account explicitly the temporal sequencing of the episodes of poverty. Using the European Community Household Panel, individuals are classified into different groups of poverty permanence, eac…
A simplified approach to estimate water retention for Sicilian soils by the Arya-Paris model
2014
Application of the Arya and Paris (AP) model to estimate the soil water retention curve requires a detailed description of the particle-size distribution (PSD) because the scale factor a, relating the pore length of an ideal soil to that of the natural one, depends on the particle size distribution parameters. For a dataset of 140 Sicilian soils that were grouped in five texture groups, the logistic and linear models were applied to evaluate a, and the water retention values predicted by the AP model were compared with the measured ones. Using the parameters proposed by Arya et al. (1999), the two models yielded similar unsystematic root mean error of estimate (RMSEu). Therefore, their pote…
Probabilité d'apparition d'un phénomène parasitaire et choix de modèles de régression logistique
2007
Epidemiological processes are now using spatial statistics and modelling tools. The main objective of most health risks studies consists in identifying potential contamination sources and factors capable of explaining their localization. Health data often prove binary (typically presence/absence) and specific methods such as binary logistic regression have to be used. This method's output consists in a probability for the pathogen of interest. A posterior classification of each sample is then conducted using a probability threshold. The method used to maximize this threshold is called the ROC curve which consists in giving a representation of the behaviour of the model and then to choose th…
Prenatal exposure to phenols and lung function, wheeze, and asthma in school-age children from 8 European birth cohorts
2019
Prenatal exposure to phenolic compounds, widely used in many consumer products, can alter lung development and increase the risk of respiratory disorders in the offspring. However, evidence is scarce and mostly focused on bisphenol-A (BPA), although there are other substitutes that could also interfere with the developing respiratory system. We aim to estimate the association between exposure to 5 phenols during pregnancy (BPA, BPAF, BPB, BPF, and BPS) and lung function, wheeze, and asthma in school-age children. We included 2685 mother-child pairs from 8 European birth cohorts. Phenols concentrations were determined in urinary maternal samples collected during pregnancy (1999-2010). Betwee…
An association model for bivariate data with application to the anlysis of university students' success.
2015
The academic success of students is a priority for all universities. We analyze the students' success at university by considering their performance in terms of both ‘qualitative performance’, measured by their mean grade, and ‘quantitative performance’, measured by university credits accumulated. These data come from an Italian University and concern a cohort of students enrolled at the Faculty of Economics. To jointly model both the marginal relationships and the association structure with covariates, we fit a bivariate ordered logistic model by penalized maximum likelihood estimation. The penalty term we use allows us to smooth the association structure and enlarge the range of possible …
A penalized approach for the bivariate ordered logistic model with applications to social and medical data
2018
Bivariate ordered logistic models (BOLMs) are appealing to jointly model the marginal distribution of two ordered responses and their association, given a set of covariates. When the number of categories of the responses increases, the number of global odds ratios to be estimated also increases, and estimation gets problematic. In this work we propose a non-parametric approach for the maximum likelihood (ML) estimation of a BOLM, wherein penalties to the differences between adjacent row and column effects are applied. Our proposal is then compared to the Goodman and Dale models. Some simulation results as well as analyses of two real data sets are presented and discussed.
Cluster-Localized Sparse Logistic Regression for SNP Data
2012
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, th…
A Comment on the Coefficient of Determination for Binary Responses
1992
Abstract Linear logistic or probit regression can be closely approximated by an unweighted least squares analysis of the regression linear in the conditional probabilities provided that these probabilities for success and failure are not too extreme. It is shown how this restriction on the probabilities translates into a restriction on the range of the coefficient of determination R 2 so that, as a consequence, R 2 is not suitable to judge the effectiveness of linear regressions with binary responses even if an important relation is present.
Subject-specific odds ratios in binomial GLMMs with continuous response
2007
In a regression context, the dichotomization of a continuous outcome variable is often motivated by the need to express results in terms of the odds ratio, as a measure of association between the response and one or more risk factors. Starting from the recent work of Moser and Coombs (Odds ratios for a continuous outcome variable without dichotomizing, Statistics in Medicine, 2004, 23, 1843-1860), in this article we explore in a mixed model framework the possibility of obtaining odds ratio estimates from a regression linear model without the need of dichotomizing the response variable. It is shown that the odds ratio estimators derived from a linear mixed model outperform those from a binom…