Solving higher-dimensional continuous time stochastic control problems by value function regression

The paper develops a method to solve higher-dimensional stochasticcontrol problems in continuous time. A finite difference typeapproximation scheme is used on a coarse grid of low discrepancypoints, while the value function at intermediate points is obtainedby regression. The stability properties of the method are discussed,and applications are given to test problems of up to 10 dimensions.Accurate solutions to these problems can be obtained on a personalcomputer.

Weighted Kernel regression

In the fixed design regression model, additional weights areconsidered for the Nadaraya--Watson and Gasser--M\"uller kernel estimators.We study their asymptotic behavior and the relationships between new andclassical estimators. For a simple family of weights, and considering theIMSE as global loss criterion, we show some possible theoretical advantages.An empirical study illustrates the performance of the weighted estimatorsin finite samples.

How wide is the gap? An investigation of gender wage differences using quantile regression

In this paper we examine the determinants of wages and decompose theobserved differences across genders into the "explained by differentcharacteristics" and "explained by different returns components"using a sample of Spanish workers. Apart from the conditionalexpectation of wages, we estimate the conditional quantile functionsfor men and women and find that both the absolute wage gap and thepart attributed to different returns at each of the quantiles, farfrom being well represented by their counterparts at the mean, aregreater as we move up in the wage range.

The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera, Arctiidae)

Species' geographic ranges are usually considered as basic units in macroecology and biogeography, yet it is still difficult to measure them accurately for many reasons. About 20 years ago, researchers started using local data on species' occurrences to estimate broad scale ranges, thereby establishing the niche modeling approach. However, there are still many problems in model evaluation and application, and one of the solutions is to find a consensus solution among models derived from different mathematical and statistical models for niche modeling, climatic projections and variable combination, all of which are sources of uncertainty during niche modeling. In this paper, we discuss this approach of ensemble forecasting and propose that it can be divided into three phases with increasing levels of complexity. Phase I is the simple combination of maps to achieve a consensual and hopefully conservative solution. In Phase II, differences among the maps used are described by multivariate analyses, and Phase III consists of the quantitative evaluation of the relative magnitude of uncertainties from different sources and their mapping. To illustrate these developments, we analyzed the occurrence data of the tiger moth, Utetheisa ornatrix (Lepidoptera, Arctiidae), a Neotropical moth species, and modeled its geographic range in current and future climates.

Market structure and hospital efficiency: Evaluating potential effects of deregulation in a national health service

In this article we examine the potential effect of market structureon hospital technical efficiency as a measure of performance controlled byownership and regulation. This study is relevant to provide an evaluationof the potential effects of recommended and initiated deregulation policiesin order to promote market reforms in the context of a European NationalHealth Service. Our goal was reached through three main empirical stages.Firstly, using patient origin data from hospitals in the region of Cataloniain 1990, we estimated geographic hospital markets through the Elzinga--Hogartyapproach, based on patient flows. Then we measured the market level ofconcentration using the Herfindahl--Hirschman index. Secondly, technicaland scale efficiency scores for each hospital was obtained specifying aData Envelopment Analysis. According to the data nearly two--thirds of thehospitals operate under the production frontier with an average efficiencyscore of 0.841. Finally, the determinants of the efficiency scores wereinvestigated using a censored regression model. Special attention waspaid to test the hypothesis that there is an efficiency improvement in morecompetitive markets. The results suggest that the number of competitors inthe market contributes positively to technical efficiency and there is someevidence that the differences in efficiency scores are attributed toseveral environmental factors such as ownership, market structure andregulation effects.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Fixed and random effects in Classical and Bayesian regression

This paper proposes a common and tractable framework for analyzingdifferent definitions of fixed and random effects in a contant-slopevariable-intercept model. It is shown that, regardless of whethereffects (i) are treated as parameters or as an error term, (ii) areestimated in different stages of a hierarchical model, or whether (iii)correlation between effects and regressors is allowed, when the sameinformation on effects is introduced into all estimation methods, theresulting slope estimator is also the same across methods. If differentmethods produce different results, it is ultimately because differentinformation is being used for each methods.

The importance of individual heterogeneity in the decomposition of measures of socioeconomic inequality in health: An approach based on quantile regression

This paper shows how recently developed regression-based methods for thedecomposition of health inequality can be extended to incorporateindividual heterogeneity in the responses of health to the explanatoryvariables. We illustrate our method with an application to the CanadianNPHS of 1994. Our strategy for the estimation of heterogeneous responsesis based on the quantile regression model. The results suggest that thereis an important degree of heterogeneity in the association of health toexplanatory variables which, in turn, accounts for a substantial percentageof inequality in observed health. A particularly interesting finding isthat the marginal response of health to income is zero for healthyindividuals but positive and significant for unhealthy individuals. Theheterogeneity in the income response reduces both overall health inequalityand income related health inequality.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

A positive theory of geographic mobility and social insurance

This paper presents a tractable dynamic general equilibrium model thatcan explain cross-country empirical regularities in geographical mobility,unemployment and labor market institutions. Rational agents vote overunemployment insurance (UI), taking the dynamic distortionary effects ofinsurance on the performance of the labor market into consideration.Agents with higher cost of moving, i.e., more attached to their currentlocation, prefer more generous UI. The key assumption is that an agent'sattachment to a location increases the longer she has resided there. UIreduces the incentive for labor mobility and increases, therefore, thefraction of attached agents and the political support for UI. The mainresult is that this self-reinforcing mechanism can give rise to multiplesteady-states-one 'European' steady-state featuring high unemployment,low geographical mobility and high unemployment insurance, and one'American' steady-state featuring low unemployment, high mobility andlow unemployment insurance.

Notes on the geographic distribution and subspecific taxonomy of Sais rosalia (Cramer) (Lepidoptera, Nymphalidae, Ithomiini), including the first records in Paraguay

Notes on the geographic distribution and subspecific taxonomy of Sais rosalia (Cramer) (Lepidoptera, Nymphalidae, Ithomiini), including the first records in Paraguay. This paper provides comments on the subspecific taxonomy and geographic distribution of Sais rosalia (Cramer, 1779) (Lepidoptera, Nymphalidae, Ithomiini), as well as an up-to-date distributional map, complemented with unpublished distributional data based on specimens deposited in the Coleção Entomológica Pe. Jesus S. Moure, Curitiba, Brazil and the Museo de Historia Natural, Lima, Peru. The following synonyms are proposed: Sais rosalia camariensis Haensch, 1905 syn. rev. as junior subjective synonym of Papilio rosalia Cramer, 1779 and Sais rosalia brasiliensis Talbot, 1928 syn. rev. as junior subjective synonym of Sais rosalia rosalinde Weymer, 1890. Additionally, the first country records of Sais rosalia in Paraguay, including the southernmost record of the species, are documented.

Constant fluctuating asymmetry but not directional asymmetry along the geographic distribution of Drosophila antonietae (Diptera, Drosophilidae)

ABSTRACT The population dynamics of a species tends to change from the core to the periphery of its distribution. Therefore, one could expect peripheral populations to be subject to a higher level of stress than more central populations (the center–periphery hypothesis) and consequently should present a higher level of fluctuating asymmetry. To test these predictions we study asymmetry in wing shape of five populations of Drosophila antonietae collected throughout the distribution of the species using fluctuating asymmetry as a proxy for developmental instability. More specifically, we addressed the following questions: (1) what types of asymmetry occur in populations of D. antonietae? (2) Does the level of fluctuating asymmetry vary among populations? (3) Does peripheral populations have a higher fluctuating asymmetry level than central populations? We used 12 anatomical landmarks to quantify patterns of asymmetry in wing shape in five populations of D. antonietae within the framework of geometric morphometrics. Net asymmetry – a composite measure of directional asymmetry + fluctuating asymmetry – varied significantly among populations. However, once net asymmetry of each population is decomposed into directional asymmetry and fluctuating asymmetry, most of the variation in asymmetry was explained by directional asymmetry alone, suggesting that populations of D. antonietae have the same magnitude of fluctuating asymmetry throughout the geographical distribution of the species. We hypothesize that larval development in rotting cladodes might play an important role in explaining our results. In addition, our study underscores the importance of understanding the interplay between the biology of a species and its geographical patterns of asymmetry.

Length of activity season drives geographic variation in body size of a widely distributed lizard

Understanding the factors that drive geographic variation in life history is an important challenge in evolutionary ecology. Here, we analyze what predicts geographic variation in life-history traits of the common lizard, Zootoca vivipara, which has the globally largest distribution range of all terrestrial reptile species. Variation in body size was predicted by differences in the length of activity season, while we found no effects of environmental temperature per se. Females experiencing relatively short activity season mature at a larger size and remain larger on average than females in populations with relatively long activity seasons. Interpopulation variation in fecundity was largely explained by mean body size of females and reproductive mode, with viviparous populations having larger clutch size than oviparous populations. Finally, body size-fecundity relationship differs between viviparous and oviparous populations, with relatively lower reproductive investment for a given body size in oviparous populations. While the phylogenetic signal was weak overall, the patterns of variation showed spatial effects, perhaps reflecting genetic divergence or geographic variation in additional biotic and abiotic factors. Our findings emphasize that time constraints imposed by the environment rather than ambient temperature play a major role in shaping life histories in the common lizard. This might be attributed to the fact that lizards can attain their preferred body temperature via behavioral thermoregulation across different thermal environments. Length of activity season, defining the maximum time available for lizards to maintain optimal performance, is thus the main environmental factor constraining growth rate and annual rates of mortality. Our results suggest that this factor may partly explain variation in the extent to which different taxa follow ecogeographic rules.

Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.