Some Additional Remarks on Statistical Properties of Cohen's d from Linear Regression

The size of the effect of the difference in two groups with respect to a variable of interest may be estimated by the classical Cohen's $d$. A recently proposed generalized estimator allows conditioning on further independent variables within the framework of a linear regression model. In this note, it is demonstrated how unbiased estimation of the effect size parameter together with a corresponding standard error may be obtained based on the non-central $t$ distribution. The portrayed estimator may be considered as a natural generalization of the unbiased Hedges' $g$. In addition, confidence interval estimation for the unknown parameter is demonstrated by applying the so-called inversion confidence interval principle. The regarded properties collapse to already known ones in case of absence of any additional independent variables. The stated remarks are illustrated with a publicly available data set

D-Vine GAM Copula based Quantile Regression with Application to Ensemble Postprocessing

Temporal, spatial or spatio-temporal probabilistic models are frequently used for weather forecasting. The D-vine (drawable vine) copula quantile regression (DVQR) is a powerful tool for this application field, as it can automatically select important predictor variables from a large set and is able to model complex nonlinear relationships among them. However, the current DVQR does not always explicitly and economically allow to account for additional covariate effects, e.g. temporal or spatio-temporal information. Consequently, we propose an extension of the current DVQR, where we parametrize the bivariate copulas in the D-vine copula through Kendall's Tau which can be linked to additional covariates. The parametrization of the correlation parameter allows generalized additive models (GAMs) and spline smoothing to detect potentially hidden covariate effects. The new method is called GAM-DVQR, and its performance is illustrated in a case study for the postprocessing of 2m surface temperature forecasts. We investigate a constant as well as a time-dependent Kendall's Tau. The GAM-DVQR models are compared to the benchmark methods Ensemble Model Output Statistics (EMOS), its gradient-boosted extension (EMOS-GB) and basic DVQR. The results indicate that the GAM-DVQR models are able to identify time-dependent correlations as well as relevant predictor variables and significantly outperform the state-of-the-art methods EMOS and EMOS-GB. Furthermore, the introduced parameterization allows using a static training period for GAM-DVQR, yielding a more sustainable model estimation in comparison to DVQR using a sliding training window. Finally, we give an outlook of further applications and extensions of the GAM-DVQR model. To complement this article, our method is accompanied by an R-package called gamvinereg

Multivariate and spatial ensemble postprocessing methods

In the recent past the state of the art in meteorology has been to produce weather forecasts from ensemble prediction systems. Forecast ensembles are generated from multiple runs of dynamical numerical weather prediction models, each with different initial and boundary conditions or parameterizations of the model. However, ensemble forecasts are not able to catch the full uncertainty of numerical weather predictions and therefore often display biases and dispersion errors and thus are uncalibrated. To account for this problem, statistical postprocessing methods have been developed successfully. However, many state of the art methods are designed for a single weather quantity at a fixed location and for a fixed forecast horizon. This work introduces extensions of two established univariate postprocessing methods, Bayesian model averaging (BMA) and Ensemble model output statistics (EMOS) to recover inter-variable and spatial dependencies from the original ensemble forecasts. For this purpose, a multi-stage procedure is proposed that can be applied for modeling dependence structures between different weather quantities as well as modeling spatial or temporal dependencies. This multi-stage procedure combines the postprocessing of the margins by the application of a univariate method as BMA or EMOS with a multivariate dependence structure, for example via a correlation matrix or via the multivariate rank structure of the original ensemble. The multivariate postprocessing procedure that models inter-variable dependence employs the UWME 8-member forecast ensemble over the North West region of the US and the standard BMA method, resulting in predictive distributions with good multivariate calibration and sharpness. The spatial postprocessing procedure is applied to temperature forecasts of the ECMWF 50-member ensemble over Germany. The procedure employs a spatially adaptive extension of EMOS, utilizing recently proposed methods for fast and accurate Bayesian estimation in a spatial setting. It yields excellent spatial univariate and multivariate calibration and sharpness. Further the method is able to capture the spatial structure of observed weather fields. Both extensions improve calibration and sharpness in comparison to the raw ensemble and to the respective standard univariate postprocessing methods

Provenienzforschung - ein Thema mit vielen Facetten : Erfahrungen im Staatlichen Museum Schwerin

Am Beispiel der Bestände der Staatlichen Museen Schwerin werden verschiedene Facetten der Provenienzforschung erläutert: so geht es ebenso um die Rückführung verlorener Objekte nach dem zweiten Weltkrieg, Dokumentation der Kriegsverluste wie um die Recherche nach Kunstgut jüdischer Eigentümer. Dass die genaue Dokumentation und ausführliche Inventarisierung auch verlorener und vermisster Sammlungsbestände eine wichtige Aufgabe für Museen ist, ist in Deutschland lange unterschätzt worden. Abgesehen von spektakulären Wiederauffinden von vermissten Werken führte die aufwändige Recherche auch zu Erkenntniszuwächsen, insbesondere über einstige Sammlungskonstellationen oder spezifische Erwerbungsumstände einzelner Kunstwerke, auch konnten Irrtümer berichtigt werden. Die Autorin plädiert für Transparenz beim Umgang mit dem Wissen über die äußerst vielschichtigen Herkunftsfragen bei Museen in Deutschland

NCAM1, TACR1 and NOS Genes and Temperament: A Study on Suicide Attempters and Controls

Suicide, one of the leading causes of death among young adults, seems to be plausibly modulated by both genetic and personality factors. The aim of this study was to dissect the potential association between genetics and temperament in a sample of 111 suicide attempters and 289 healthy controls. We focused on 4 genes previously investigated in association with suicide on the same sample: the nitric oxide synthase 1 and 3 (NOS1 and NOS3), the neuronal cell adhesion molecule 1 (NCAM1), and the tachykinin receptor 1 (TACR1) genes. In particular, we investigated whether a set of genetic variants in these genes (NOS1 : rs2682826, rs1353939, rs693534; NOS3 : rs2070744, rs1799983, rs891512; NCAM1 : rs2301228, rs1884, rs1245113, rs1369816, rs2196456, rs584427; TACR1 : rs3771810, rs3771825, rs726506, rs1477157) were associated with temperamental traits at the Temperament and Character Inventory (TCI). No strong evidence was found for the association between TCI personality traits and the polymorphisms considered in the 4 genes, with the exception of an association between reward dependence trait and the rs2682826 SNP in NOS1 in the healthy sample. However, this result could be plausibly interpreted as a false-positive finding. In conclusion, our study did not support the thesis of a direct modulation of these genes on temperament; however, further studies on larger samples are clearly required in order to confirm our preliminary findings and to exclude any possible minor influence. Copyright (C) 2011 S. Karger AG, Base