Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis

This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on microfirms' performance and produce a clear characterization of who benefits from these programs.

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.

Genetic structure and systematic relationships within the Ophrys fuciflora aggregate (Orchidaceae: Orchidinae): high diversity in Kent and a wind-induced discontinuity bisecting the Adriatic

A recent phylogenetic study based on multiple datasets is used as the framework for a more detailed examination of one of the ten molecularly circumscribed groups identified, the Ophrys fuciflora aggregate. The group is highly morphologically variable, prone to phenotypic convergence, shows low levels of sequence divergence and contains an unusually large proportion of threatened taxa, including the rarest Ophrys species in the UK. The aims of this study were to (a) circumscribe minimum resolvable genetically distinct entities within the O. fuciflora aggregate, and (b) assess the likelihood of gene flow between genetically and geographically distinct entities at the species and population levels. Fifty-five accessions sampled in Europe and Asia Minor from the O. fuciflora aggregate were studied using the AFLP genetic fingerprinting technique to evaluate levels of infraspecific and interspecific genetic variation and to assess genetic relationships between UK populations of O. fuciflora s.s. in Kent and in their continental European and Mediterranean counterparts. The two genetically and geographically distinct groups recovered, one located in England and central Europe and one in south-eastern Europe, are incongruent with current species delimitation within the aggregate as a whole and also within O. fuciflora s.s. Genetic diversity is higher in Kent than in the rest of western and central Europe. Gene flow is more likely to occur between populations in closer geographical proximity than those that are morphologically more similar. Little if any gene flow occurs between populations located in the south-eastern Mediterranean and those dispersed throughout the remainder of the distribution, revealing a genetic discontinuity that runs north-south through the Adriatic. This discontinuity is also evident in other clades of Ophrys and is tentatively attributed to the long-term influence of prevailing winds on the long-distance distribution of pollinia and especially seeds. A cline of gene flow connects populations from Kent and central and southern Europe; these individuals should therefore be considered part of an extensive meta-population. Gene flow is also evident among populations from Kent, which appear to constitute a single metapopulation. They show some evidence of hybridization, and possibly also introgression, with O. apifera.

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.

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.