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Orthotrichumcamanchacanum is presented as a newly described species from Chile. The species is primarily distinguished by its emergent capsule with cryptoporous stomata, a double peristome, linear-lanceolate stem leaves with a long hyaline aristae in apex, conspicuously differentiated perichaetial leaves, and a densely hairy vaginula. The species was discovered in the mountain massif of the Andes in the Coquimbo region, notable for its unique climatic conditions. Molecular data and a brief discussion comparing the newly described species with the most closely related taxa are also provided.
RESUMEN
Computing the agreement between 2 continuous sequences is of great interest in statistics when comparing 2 instruments or one instrument with a gold standard. The probability of agreement quantifies the similarity between 2 variables of interest, and it is useful for determining what constitutes a practically important difference. In this article, we introduce a generalization of the PA for the treatment of spatial variables. Our proposal makes the PA dependent on the spatial lag. We establish the conditions for which the PA decays as a function of the distance lag for isotropic stationary and nonstationary spatial processes. Estimation is addressed through a first-order approximation that guarantees the asymptotic normality of the sample version of the PA. The sensitivity of the PA with respect to the covariance parameters is studied for finite sample size. The new method is described and illustrated with real data involving autumnal changes in the green chromatic coordinate (Gcc), an index of "greenness" that captures the phenological stage of tree leaves, is associated with carbon flux from ecosystems, and is estimated from repeated images of forest canopies.
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Ecosistema , Bosques , Probabilidad , Tamaño de la MuestraRESUMEN
The concordance correlation coefficient (CCC) and the probability of agreement (PA) are two frequently used measures for evaluating the degree of agreement between measurements generated by two different methods. In this paper, we consider the CCC and the PA using the bivariate normal distribution for modeling the observations obtained by two measurement methods. The main aim of this paper is to develop diagnostic tools for the detection of those observations that are influential on the maximum likelihood estimators of the CCC and the PA using the local influence methodology but not based on the likelihood displacement. Thus, we derive first- and second-order measures considering the case-weight perturbation scheme. The proposed methodology is illustrated through a Monte Carlo simulation study and using a dataset from a clinical study on transient sleep disorder. Empirical results suggest that under certain circumstances first-order local influence measures may be more powerful than second-order measures for the detection of influential observations.
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Biometría/métodos , Ensayos Clínicos como Asunto , Análisis de Datos , Humanos , Funciones de Verosimilitud , Modelos Estadísticos , Método de Montecarlo , Probabilidad , Trastornos del Sueño-Vigilia/diagnósticoRESUMEN
In 2005 the city of Bogota, Colombia, introduced three conditional cash transfer programs for secondary schooling, randomly assigning socioeconomically disadvantaged students to different payment structures. We show, through administrative data, that forcing families to save one-third of the transfer increases long-term human capital accumulation by means of additional tertiary education-which is not incentivized-, casting doubt on conditionalities as a driving mechanism. Directly incentivizing on-time tertiary enrollment does no better than forcing families to save a portion of the transfer Whereas forcing families to save increases enrollment in four-year universities, incentivizing tertiary enrollment only increases enrollment in low-quality colleges.