Library of medium-resolution fiber optic echelle spectra of F, G, K, and M field dwarfs to giants stars

We present a library of Penn State Fiber Optic Echelle (FOE) observations of a sample of field stars with spectral types F to M and luminosity classes V to I. The spectral coverage is from 3800 AA to 10000 AA with nominal a resolving power 12000. These spectra include many of the spectral lines most widely used as optical and near-infrared indicators of chromospheric activity such as the Balmer lines (H_alpha, H_beta), Ca II H & K, Mg I b triplet, Na I D_{1} and D_{2}, He I D_{3}, and Ca II IRT lines. There are also a large number of photospheric lines, which can also be affected by chromospheric activity, and temperature sensitive photospheric features such as TiO bands. The spectra have been compiled with the goal of providing a set of standards observed at medium resolution. We have extensively used such data for the study of active chromosphere stars by applying a spectral subtraction technique. However, the data set presented here can also be utilized in a wide variety of ways ranging from radial velocity templates to study of variable stars and stellar population synthesis. This library can also be used for spectral classification purposes and determination of atmospheric parameters (T_eff, log{g}, [Fe/H]). A digital version of all the fully reduced spectra is available via ftp and the World Wide Web (WWW) in FITS format.Comment: Latex file with 17 pages, 4 figures. Full postscript (text and figures) available at http://www.ucm.es/info/Astrof/fgkmsl/FOEfgkmsl.html To be published in ApJ

Social and Emotional Competencies and Science Performance in the USA: Evidence from PISA 2015

This paper asks whether students with different socioemotional learning (SEL) profiles perform differently in science. Using latent class analysis, we found three distinct groups of students: a majority of students who are relatively unmotivated and isolated, a sizeable group of students who are strong co-operators, and a relatively small group of students who are highly motivated and enjoy science, but do not value cooperation. After controlling for student and family covariates, as well as classroom, teaching and school leadership and institutional variables, the highly motivated, individualist group substantially outperformed the isolated group, with the co-operator group having intermediate performance. These SEL related differences in science performance were large, larger than performance differences associated with socioeconomic variables

Robust misspecification tests for the Heckman’s two-step estimator

We construct and evaluate LM and Neyman’s C(α) tests based on bivariate Edgeworth expansions for the consistency of the Heckman’s two-step estimator in selection models, that is, for the marginal normality and linearity of the conditional expectation of the error terms. The proposed tests are robust to local misspecification in nuisance distributional parameters. Monte Carlo results show that instead of testing bivariate normality, testing marginal normality and linearity of the conditional expectations separately have a better size performance. Moreover, the robust variants of the tests have better size and similar power to non-robust tests, which determines that these tests can be successfully applied to detect specific departures from the null model of bivariate normality. We apply the tests procedures to women’s labor supply data

Improving DISPGB Algorithm Using the Discriminant Ideal

In 1992, V. Weispfenning proved the existence of Comprehensive Groebner Bases (CGB) and gave an algorithm to compute one. That algorithm was not very efficient and not canonical. Using his suggestions, A. Montes obtained in 2002 a more efficient algorithm (DISPGB) for Discussing Parametric Groebner Bases. Inspired in its philosophy, V. Weispfenning defined, in 2002, how to obtain a Canonical Comprehensive Groebner Basis (CCGB) for parametric polynomial ideals, and provided a constructive method. In this paper we use Weispfenning's CCGB ideas to make substantial improvements on Montes DISPGB algorithm. It now includes rewriting of the discussion tree using the Discriminant Ideal and provides a compact and effective discussion. We also describe the new algorithms in the DPGB library containing the improved DISPGB as well as new routines to check whether a given basis is a CGB or not, and to obtain a CGB. Examples and tests are also provided.Comment: 21 pages, see also http://www-ma2.upc.edu/~montes