715 research outputs found
Are there asymmetries in the effects of training on the conditional male wage distribution?
Recent studies have used quantile regression (QR) techniques to estimate the impact of education on the location, scale and shape of the conditional wage distribution. In our paper we investigate the degree to which work-related training – another important form of human capital – affects the location, scale and shape of the conditional wage distribution. Using the first six waves of the European Community Household Panel, we utilise both ordinary least squares and QR techniques to estimate associations between work-related training and wages for private sector men in ten European Union countries. Our results show that, for the majority of countries, there is a fairly uniform association between training and hourly wages across the conditional wage distribution. However, there are considerable differences across countries in mean associations between training and wages
Bayesian lasso binary quantile regression
In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers, heteroskedasticity or other anomalies of the latent response. The method proposed in this study overcomes these problems in an attractive and straightforward way. A Laplace likelihood and Laplace priors for the regression parameters are proposed and estimated with Bayesian Markov Chain Monte Carlo. The resulting model is equivalent to the frequentist lasso procedure. A conceptional result is that by doing so, the binary regression model is moved from a Gaussian to a full Laplacian framework without sacrificing much computational efficiency. In addition, an efficient Gibbs sampler to estimate the model parameters is proposed that is superior to the Metropolis algorithm that is used in previous studies on Bayesian binary quantile regression. Both the simulation studies and the real data analysis indicate that the proposed method performs well in comparison to the other methods. Moreover, as the base model is binary quantile regression, a much more detailed insight in the effects of the covariates is provided by the approach. An implementation of the lasso procedure for binary quantile regression models is available in the R-package bayesQR
Changes in extreme sea-levels in the Baltic Sea
In a climate change context, changes in extreme sea-levels rather than changes in the mean are of particular interest from the coastal protection point of view. In this work, extreme sea-levels in the Baltic Sea are investigated based on daily tide gauge records for the period 1916–2005 using the annual block maxima approach. Extreme events are analysed based on the generalised extreme value distribution considering both stationary and time-varying models. The likelihood ratio test is applied to select between stationary and non-stationary models for the maxima and return values are estimated from the final model. As an independent and complementary approach, quantile regression is applied for comparison with the results from the extreme value approach. The rates of change in the uppermost quantiles are in general consistent and most pronounced for the northernmost stations
Global distribution and bioclimatic characterization of alpine biomes
Although there is a general consensus on the distribution and ecological features of terrestrial biomes, the allocation of alpine ecosystems in the global biogeographic system is still unclear. Here, we delineate a global map of alpine areas above the treeline by modelling regional treeline elevation at 30 m resolution, using global forest cover data and quantile regression. We then used global datasets to 1) assess the climatic characteristics of alpine ecosystems using principal component analysis, 2) define bioclimatic groups by an optimized cluster analysis and 3) evaluate patterns of primary productivity based on the normalized difference vegetation index. As defined here, alpine biomes cover 3.56 Mkm(2) or 2.64% of land outside Antarctica. Despite temperature differences across latitude, these ecosystems converge below a sharp threshold of 5.9 degrees C and towards the colder end of the global climatic space. Below that temperature threshold, alpine ecosystems are influenced by a latitudinal gradient of mean annual temperature and they are climatically differentiated by seasonality and continentality. This gradient delineates a climatic envelope of global alpine biomes around temperate, boreal and tundra biomes as defined in Whittaker's scheme. Although alpine biomes are similarly dominated by poorly vegetated areas, world ecoregions show strong differences in the productivity of their alpine belt irrespectively of major climate zones. These results suggest that vegetation structure and function of alpine ecosystems are driven by regional and local contingencies in addition to macroclimatic factors
A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data
<p>Abstract</p> <p>Background</p> <p>Identification of biomarkers among thousands of genes arrayed for disease classification has been the subject of considerable research in recent years. These studies have focused on disease classification, comparing experimental groups of effected to normal patients. Related experiments can be done to identify tissue-restricted biomarkers, genes with a high level of expression in one tissue compared to other tissue types in the body.</p> <p>Results</p> <p>In this study, cartilage was compared with ten other body tissues using a two color array experimental design. Thirty-seven probe sets were identified as cartilage biomarkers. Of these, 13 (35%) have existing annotation associated with cartilage including several well-established cartilage biomarkers. These genes comprise a useful database from which novel targets for cartilage biology research can be selected. We determined cartilage specific Z-scores based on the observed M to classify genes with Z-scores ≥ 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.</p> <p>Conclusion</p> <p>Quantile regression is a promising method for the analysis of two color array experiments that compare multiple samples in the absence of biological replicates, thereby limiting quantifiable error. We used a nonparametric approach to reveal the relationship between percentiles of M and A, where M is log<sub>2</sub>(R/G) and A is 0.5 log<sub>2</sub>(RG) with R representing the gene expression level in cartilage and G representing the gene expression level in one of the other 10 tissues. Then we performed linear quantile regression to identify genes with a cartilage-restricted pattern of expression.</p
Individual Heterogeneity in the Returns to Schooling: Instrumental Variables Quantile Regression Using Twins Data
Considerable effort has been exercised in estimating mean returns to education while carefully considering biases arising from unmeasured ability and measurement error. Recent work has investigated whether there are variations from the “mean” return to education across the population with mixed results. We use an instrumental variables estimator for quantile regression on a sample of twins to estimate an entire family of returns to education at different quantiles of the conditional distribution of wages while addressing simultaneity and measurement error biases. We test whether there is individual heterogeneity in returns to education and find that: more able individuals obtain more schooling and that higher ability individuals (those further to the right in the conditional distribution of wages) have higher returns to schooling consistent with a non-trivial interaction between schooling and unobserved abilities in the generation of earnings. The estimated returns are never lower than 9 percent and can be as high as 13 percent at the top of the conditional distribution of wages but they vary significantly only along the lower to middle quantiles. Our findings may have meaningful implications for the design of educational policies
Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models
Value at Risk models with long memory features and their economic performance
We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms
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