Social Capital and Subjective Wellbeing in Europe: A New Approach on Social Capital

This paper aims to analyze the relationship between the various dimensions of social capital and subjective wellbeing. Data used in this study come from the fourth wave of the European Social Survey and different measures of wellbeing are used to take account of both the cognitive and affective processes of individual wellbeing (i.e. life satisfaction, happiness, and subjective wellbeing). A factor analysis is performed to summarize information coming from a large set of variables into different components corresponding to each dimension of social capital (i.e. networks, norms, and trust). Among the results, we find that the impact of social capital on subjective wellbeing differ depending on the component of social capital which is under analysis. In particular, social networks, social trust and institutional trust are the components that show a higher correlation with subjective wellbeing. Furthermore, in addition to the positive effects of the individual variables, our results suggest that social capital at the aggregate level positively correlates with individual wellbeing, thus pointing to an external or environmental effect of social capital.     

A unified epistemological theory of information processing

Theory and Decision - What does it mean for an agent faced with choice under uncertainty to “know” something? While a variety of mathematical methods are available to construct formal...     

Adaptive PORT–MVRB estimation: an empirical comparison of two heuristic algorithms

In this article, we deal with an empirical comparison of two data-driven heuristic procedures of estimation of a positive extreme value index (EVI), working thus with heavy right tails. The semi-parametric EVI-estimators under consideration, the so-called peaks over random threshold (PORT)–minimum-variance reduced-bias (MVRB) EVI-estimators, are location and scale-invariant estimators, based on the PORT methodology applied to second-order MVRB EVI-estimators. Trivial adaptations of these algorithms make them work for a similar estimation of other parameters of extreme events, such as the Value-at-Risk at a level p, the expected shortfall and the probability of exceedance of a high level x, among others. Applications to simulated data sets and to real data sets in the field of finance are provided.     

A bootstrap test for single index models

Single index models are frequently used in econometrics and biometrics. Logit and Probit models arc special cases with fixed link functions. In this paper we consider a bootstrap specification test that detects nonparametric deviations of the link function. The bootstrap is used with the aim to rind a more accurate distribution under the null than the normal approximation. We prove that the statistic and its bootstrapped version have the same asymptotic distribution. In a simulation study we show that the bootstrap is able to capture the negative bias and the skewness of the test statistic. It yields better approximations to the true critical values and consequently it has a more accurate level than the normal approximation.     

Standard statistical tools for the breed allocation problem

Modern technologies are frequently used in order to deal with new genomic problems. For instance, the STRUCTURE software is usually employed for breed assignment based on genetic information. However, standard statistical techniques offer a number of valuable tools which can be successfully used for dealing with most problems. In this paper, we investigated the capability of microsatellite markers for individual identification and their potential use for breed assignment of individuals in seventy Lidia breed lines and breeders. Traditional binomial logistic regression is applied to each line and used to assign one individual to a particular line. In addition, the area under receiver operating curve (AUC) criterion is used to measure the capability of the microsatellite-based models to separate the groups. This method allows us to identify which microsatellite loci are related to each line. Overall, only one subject was misclassified or a 99.94% correct allocation. The minimum observed AUC was 0.986 with an average of 0.997. These results suggest that our method is competitive for animal allocation and has some interpretative advantages and a strong relationship with methods based on SNPs and related techniques.     

Estimating and Validating Nonlinear Regression Metamodels in Simulation

Frequently, the main objective of statistically designed simulation experiments is to estimate and validate regression metamodels, where the regressors are functions of the design variables and the dependent variable is the system response. In this article, a weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel is formulated and evaluated. Since the validity of a fitted regression model must be tested, a method for validating nonlinear regression simulation metamodels is presented. This method is a generalization of the cross-validation test proposed by Kleijnen (1983 Kleijnen , J. P. C. ( 1983 ). Cross-validation using the t statistic . European Journal of Operational Research 13 : 133 – 141 .[Crossref] , [Google Scholar]) in the context of linear regression metamodels. One drawback of the cross-validation strategy is the need to perform a large number of nonlinear regressions, if the number of experimental points is large. In this article, cross-validation is implemented using only one nonlinear regression. The proposed statistical analysis allows us to obtain Scheffé-type simultaneous confidence intervals for linear combinations of the metamodel's unknown parameters. Using the well-known M/M/1 example, a metamodel is built and validated with the aid of the proposed procedure.     

A new mathematical approach for the estimation of the AUC and its variability under different experimental designs in preclinical studies

The aim of the present work was to develop a new mathematical method for estimating the area under the curve (AUC) and its variability that could be applied in different preclinical experimental designs and amenable to be implemented in standard calculation worksheets. In order to assess the usefulness of the new approach, different experimental scenarios were studied and the results were compared with those obtained with commonly used software: WinNonlin® and Phoenix WinNonlin®. The results do not show statistical differences among the AUC values obtained by both procedures, but the new method appears to be a better estimator of the AUC standard error, measured as the coverage of 95% confidence interval. In this way, the new proposed method demonstrates to be as useful as WinNonlin® software when it was applicable. Copyright © 2011 John Wiley & Sons, Ltd.     

Development of Tactile Responses in Human Preterm and Full‐Term Infants From 30 to 40 Weeks Postconceptional Age

Maturation of tactile sensitivity prior to term was examined in 36 preterm and 13 full‐term infants using a fixed‐trial, habituation procedure. Each infant was presented with a series of 8 habituation (arm stroke), 2 novel (arm lift), and 2 recovery (arm stroke) stimulus trials while heart rate and body movements were recorded. Maturation was observed with a gradual increase in the magnitude of the stimulus‐elicited cardiac acceleration and cardiac‐movement coupling from 30 to 40 weeks postconceptional age. The majority of infants displayed habituation—an excitatory response (heart rate acceleration and body movement)—to the initial presentation of a tactile stimulus, response decline with repeated stimulations, and renewed response to a novel stimulus. A substantial number of infants (40%) failed to respond initially to the tactile stimulus, increased responding over several stimulus presentations, and failed to discriminate the presentation of a novel stimulus. We speculate that these differences in response patterns observed over all ages represent individual difference in the perception of stimulus intensity.     

Conditional tests for elliptical symmetry using robust estimators

This paper presents a procedure for testing the hypothesis that the underlying distribution of the data is elliptical when using robust location and scatter estimators instead of the sample mean and covariance matrix. Under mild assumptions that include elliptical distributions without first moments, we derive the test statistic asymptotic behavior under the null hypothesis and under special alternatives. Numerical experiments allow to compare the behavior of the tests based on the sample mean and covariance matrix with that based on robust estimators, under various elliptical distributions and different alternatives. We also provide a numerical comparison with other competing tests.     

Small area estimation of poverty indicators

The authors propose to estimate nonlinear small area population parameters by using the empirical Bayes (best) method, based on a nested error model. They focus on poverty indicators as particular nonlinear parameters of interest, but the proposed methodology is applicable to general nonlinear parameters. They use a parametric bootstrap method to estimate the mean squared error of the empirical best estimators. They also study small sample properties of these estimators by model‐based and design‐based simulation studies. Results show large reductions in mean squared error relative to direct area‐specific estimators and other estimators obtained by “simulated” censuses. The authors also apply the proposed method to estimate poverty incidences and poverty gaps in Spanish provinces by gender with mean squared errors estimated by the mentioned parametric bootstrap method. For the Spanish data, results show a significant reduction in coefficient of variation of the proposed empirical best estimators over direct estimators for practically all domains. The Canadian Journal of Statistics 38: 369–385; 2010 © 2010 Statistical Society of Canada