Determinants of fruit and vegetable intake in England: a re-examination based on quantile regression

Objective To examine die sociodemographic determinants of fruit and vegetable (F&V) consumption in England and determine the differential effects of socioeconomic variables at various parts of the intake distribution, with a special focus on severely inadequate intakes Design Quantile regression, expressing F&V intake as a function of sociodemographic variables, is employed. Here, quantile regression flexibly allows variables such as ethnicity to exert effects on F&V intake that. vary depending oil existing levels of intake. Setting The 2003 Health survey of England. Subjects Data were from 11044 adult individuals. Results The influence of particular sociodemographic variables is found to vary significantly across the intake distribution We conclude that women consume more F&V than men, Asians and Hacks mole dian Whites, co-habiting individuals more than single-living ones Increased incomes and education also boost intake However, the key general finding of the present study is that the influence of most variables is relatively weak in the area of greatest concern, i e among those with the most inadequate intakes in any reference group. Conclusions. Our findings emphasise the importance of allowing the effects of socio-economic drivers to vary across the intake distribution The main finding, that variables which exert significant influence on F&V Intake at other parts Of the conditional distribution have a relatively weak influence at the lower tail, is cause for concern. It implies that in any defined group, those consuming the lease F&V are hard to influence using compaigns or policy levers.

A study of the effect of heat source location in a ventilated room using multiple regression analysis

Multiple regression analysis is a statistical technique which allows to predict a dependent variable from m ore than one independent variable and also to determine influential independent variables. Using experimental data, in this study the multiple regression analysis is applied to predict the room mean velocity and determine the most influencing parameters on the velocity. More than 120 experiments for four different heat source locations were carried out in a test chamber with a high level wall mounted air supply terminal at air change rates 3-6 ach. The influence of the environmental parameters such as supply air momentum, room heat load, Archimedes number and local temperature ratio, were examined by two methods: a simple regression analysis incorporated into scatter matrix plots and multiple stepwise regression analysis. It is concluded that, when a heat source is located along the jet centre line, the supply momentum mainly influences the room mean velocity regardless of the plume strength. However, when the heat source is located outside the jet region, the local temperature ratio (the inverse of the local heat removal effectiveness) is a major influencing parameter.

An experimental comparison between rival theories of rapid automatized naming performance and its relationship to reading

Two studies investigated the degree to which the relationship between rapid automatized naming (RAN) performance and reading development is driven by shared phonological processes. Study 1 assessed RAN, phonological awareness, and reading performance in 1010 7- to -10 year-olds. Results showed that RAN deficits occurred in the absence of phonological awareness deficits. These were accompanied by modest reading delays. In structural equation modeling, solutions where RAN was subsumed within a phonological processing factor did not provide a good fit to the data, suggesting that processes outside phonology may drive RAN performance and its association with reading. Study 2 investigated Kail’s proposal that speed of processing underlies this relationship. Children with single RAN deficits showed slower speed of processing than did closely matched controls performing normally on RAN. However, regression analysis revealed that RAN made a unique contribution to reading even after accounting for processing speed. Theoretical implications are discussed.

Social factors and postpartum depression in Khayelitsha, Cape Town

Social factors, including poverty, are known risk factors for depression. In a previous study conducted in Khayelitsha, a very poor peri-urban settlement near Cape Town, a 34.7% prevalence rate for postpartum depression was found, roughly three times the expected rate internationally. This article is a report on a logistical regression analysis, showing that the odds ratios for the probability of maternal depression at two months were: for the infant being unwanted, OR=4.33, 95% CI: (1.75; 11.60); for the father's negative attitude towards the infant, OR=6.03, 95% CI: (2.01; 20.09); and for the mother cohabiting with (as opposed to not living with) a male partner, OR=2.77, 95% CI: (1.08; 7.69). The odds ratios for the probability of the mother being insensitive towards the infant at two months were: for the mother aged 20 to 24 years, OR=0.40, 95% CI: (0.10; 1.42); for the mother aged 25 to 29 years, OR=0.24, 95% CI: (0.06; 0.77); for the mother aged 30 years or older, OR=0.27, 95% CI: (0.07; 0.90); and for the mother receiving no help from her partner, OR=2.12, 95% CI: (1.05; 4.33). Since data were collected cross-sectionally, it is not possible to draw conclusions about causal pathways. The findings support further investigation into the precursors of, and risk factors for, postpartum depression amongst poor South African women.

The effects of maternal social phobia on mother-infant interactions and infant social responsiveness

Background: Social phobia aggregates in families. The genetic contribution to intergenerational transmission is modest, and parenting is considered important. Research on the effects of social phobia on parenting has been subject to problems of small sample size, heterogeneity of samples and lack of specificity of observational frameworks. We addressed these problems in the current study.Methods: We assessed mothers with social phobia (N = 84) and control mothers (N = 89) at 10 weeks in face-to-face interactions with their infants, and during a social challenge, namely, engaging with a stranger. We also assessed mothers with generalised anxiety disorder (GAD) (N = 50). We examined the contribution to infant social responsiveness of early infant characteristics (neonatal irritability), as well as maternal behaviour. Results: Mothers with social phobia were no less sensitive to their infants during face-to-face interactions than control mothers, but when interacting with the stranger they appeared more anxious, engaged less with the stranger themselves, and were less encouraging of the infant's interaction with the stranger; infants of index mothers also showed reduced social responsiveness to the stranger. These differences did not apply to mothers with GAD and their infants. Regression analyses showed that the reduction in social responsiveness in infants of mothers with social phobia was predicted by neonatal irritability and the degree to which the mother encouraged the infant to interact with the stranger.Conclusions: Mothers with social phobia show specific parenting difficulties, and their infants show early signs of reduced social responsiveness that are related to both individual infant differences and a lack of maternal encouragement to engage in social interactions.

Regression, developmental trajectory and associated problems in disorders in the autism spectrum: the SNAP study

We report rates of regression and associated findings in a population derived group of 255 children aged 9-14 years, participating in a prevalence study of autism spectrum disorders (ASD); 53 with narrowly defined autism, 105 with broader ASD and 97 with non-ASD neurodevelopmental problems, drawn from those with special educational needs within a population of 56,946 children. Language regression was reported in 30% with narrowly defined autism, 8% with broader ASD and less than 3% with developmental problems without ASD. A smaller group of children were identified who underwent a less clear setback. Regression was associated with higher rates of autistic symptoms and a deviation in developmental trajectory. Regression was not associated with epilepsy or gastrointestinal problems.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.