Gibbs posterior for variable selection in high-dimensional classification and data mining

In the popular approach of "Bayesian variable selection" (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of practical interest (such as the classification error) and aims at minimizing a risk function without modeling the data probabilistically. This can improve the performance over the usual Bayesian approach, which depends on a probability model which may be misspecified. Conditions will be provided to achieve good risk performance, even in the presence of high dimensionality, when the number of candidate variables "$K$" can be much larger than the sample size "$n$." In addition, we develop a convenient Markov chain Monte Carlo algorithm to implement BVS with the Gibbs posterior.Comment: Published in at http://dx.doi.org/10.1214/07-AOS547 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions

This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. The IHME models have received extensive attention in social and mass media, and have influenced policy makers at the highest levels of the United States government. For effective policy making the accurate assessment of uncertainty, as well as accurate point predictions, are necessary because the risks inherent in a decision must be taken into account, especially in the present setting of a novel disease affecting millions of lives. To assess the accuracy of the IHME models, we examine both forecast accuracy as well as the predictive performance of the 95% prediction intervals provided by the IHME models. We find that the initial IHME model underestimates the uncertainty surrounding the number of daily deaths substantially. Specifically, the true number of next day deaths fell outside the IHME prediction intervals as much as 70% of the time, in comparison to the expected value of 5%. In addition, we note that the performance of the initial model does not improve with shorter forecast horizons. Regarding the updated models, our analyses indicate that the later models do not show any improvement in the accuracy of the point estimate predictions. In fact, there is some evidence that this accuracy has actually decreased over the initial models. Moreover, when considering the updated models, while we observe a larger percentage of states having actual values lying inside the 95% prediction intervals (PI), our analysis suggests that this observation may be attributed to the widening of the PIs. The width of these intervals calls into question the usefulness of the predictions to drive policy making and resource allocation

Unusual Shubnikov-de Haas oscillations in BiTeCl

We report measurements of Shubnikov-de Haas (SdH) oscillations in single crystals of BiTeCl at magnetic fields up to 31 T and at temperatures as low as 0.4 K. Two oscillation frequencies were resolved at the lowest temperatures, $F_{1}=65 \pm 4$ Tesla and $F_{2}=156 \pm 5$ Tesla. We also measured the infrared optical reflectance $\left(\cal R(\omega)\right)$ and Hall effect; we propose that the two frequencies correspond respectively to the inner and outer Fermi sheets of the Rashba spin-split bulk conduction band. The bulk carrier concentration was $n_{e}\approx1\times10^{19}$ cm$^{-3}$ and the effective masses $m_{1}^{*}=0.20 m_{0}$ for the inner and $m_{2}^{*}=0.27 m_{0}$ for the outer sheet. Surprisingly, despite its low effective mass, we found that the amplitude of $F_{2}$ is very rapidly suppressed with increasing temperature, being almost undetectable above $T\approx4$ K

Far-Infrared Conductivity Measurements of Pair Breaking in Superconducting Nb0.5_{0.5}Ti0.5_{0.5}N Thin Films Induced by an External Magnetic Field

We report the complex optical conductivity of a superconducting thin-film of Nb$_{0.5}$Ti$_{0.5}$N in an external magnetic field. The field was applied parallel to the film surface and the conductivity extracted from far-infrared transmission and reflection measurements. The real part shows the superconducting gap, which we observe to be suppressed by the applied magnetic field. We compare our results with the pair-breaking theory of Abrikosov and Gor'kov and confirm directly the theory's validity for the optical conductivity.Comment: 4 pages, 3 figure

Bulk Fermi surface and electronic properties of Cu0.07_{0.07}Bi2_{2}Se3_{3}

The electronic properties of Cu$_{0.07}$Bi$_{2}$Se$_{3}$ have been investigated using Shubnikov-de Haas and optical reflectance measurements. Quantum oscillations reveal a bulk, three-dimensional Fermi surface with anisotropy $k^{c}_{F}/k^{ab}_{F}\approx$ 2 and a modest increase in free-carrier concentration and in scattering rate with respect to the undoped Bi$_{2}$Se$_{3}$, also confirmed by reflectivity data. The effective mass is almost identical to that of Bi$_{2}$Se$_{3}$. Optical conductivity reveals a strong enhancement of the bound impurity bands with Cu addition, suggesting that a significant number of Cu atoms enter the interstitial sites between Bi and Se layers or may even substitute for Bi. This conclusion is also supported by X-ray diffraction measurements, where a significant increase of microstrain was found in Cu$_{0.07}$Bi$_{2}$Se$_{3}$, compared to Bi$_{2}$Se$_{3}$.Comment: Accepted to Phys. Rev B (R