A spatial analysis of multivariate output from regional climate models

Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial dependencies, including the cross-dependencies between variables. We demonstrate this statistical model on an ensemble arising from a regional-climate-model experiment over the western United States, and we focus on the projected change in seasonal temperature and precipitation over the next 50 years.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS369 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Extremal Quantum Correlations and Cryptographic Security

We investigate a fundamental property of device independent security in quantum cryptography by characterizing probability distributions which are necessarily independent of the measurement results of any eavesdropper. We show that probability distributions that are secure in this sense are exactly the extremal quantum probability distributions. This allows us to give a characterization of security in algebraic terms. We apply the method to common examples for two-party as well as multi-party setups and present a scheme for verifying security of probability distributions with two parties, two measurement settings, and two outcomes.Comment: 7 pages, 2 figures, revised version, accepted for publication in Phys. Rev. Let

spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields

spam is an R package for sparse matrix algebra with emphasis on a Cholesky factorization of sparse positive definite matrices. The implemantation of spam is based on the competing philosophical maxims to be competitively fast compared to existing tools and to be easy to use, modify and extend. The first is addressed by using fast Fortran routines and the second by assuring S3 and S4 compatibility. One of the features of spam is to exploit the algorithmic steps of the Cholesky factorization and hence to perform only a fraction of the workload when factorizing matrices with the same sparsity structure. Simulations show that exploiting this break-down of the factorization results in a speed-up of about a factor 5 and memory savings of about a factor 10 for large matrices and slightly smaller factors for huge matrices. The article is motivated with Markov chain Monte Carlo methods for Gaussian Markov random fields, but many other statistical applications are mentioned that profit from an efficient Cholesky factorization as well.

Pericardial biopsy and fenestration

Employing a video thoracoscopic pericardial fenestration constitutes a promising technique for the investigation and treatment of chronic pericardial effusions. It combines the benefit of low invasiveness with the advantages of open biopsy. The procedure simultaneously allows both an accurate diagnosis under visual control (inspection, aspiration, well targeted biopsy of pathological processes) and the performance of effective therapeutic intervention. Without imposing unacceptable stress, it also facilitates rapid symptom relief in patients with advanced malignant disease whose general condition is severely impaire

Zinc Extraction potential of two common crop plants, Nicotiana tabacum and Zea mays

A field study was conducted to investigate the efficiency of Zn phytoextraction by Nicotiana tabacum and Zea mays from a soil that had been artificially contaminated by different amounts of ZnSO4 (0, 50, 150, 350, 750 and 1550 mg kg−1 soil) 10 years prior to the present cropping. Increased NaNO3-extractable Zn in soil translated well into shoot concentrations (dry matter) in plants. Zn uptake by Z. mays increased linearly with increasing NaNO3-extractable Zn in soil, while for N. tabacum the increase could be described by a Langmuir isotherm. While Z. mays showed no significant decrease in biomass production up to the highest contamination level in soil, N. tabacum responded with a reduction of plant growth of about 50% compared with control plants at the highest Zn concentrations in soil. Maximum removal of Zn was 13 kg ha−1 y−1 with Z. mays and 11 kg ha−1 y−1 with N. tabacum. Calculated time required to reduce soil Zn from 350 to 150 mg kg−1 was about 55 years for N. tabacum and about 63 years for Z. mays at a soil pH of 4.8. At higher soil pH of 6.0 calculated decontamination time was about 87 years for N. tabacum and more than 200 years for Z. mays. Only small amounts of Zn were translocated into the seeds of N. tabacum and cobs of Z. mays. Therefore, corn cobs of Z. mays could be safely used for fodder and the seeds of N. tabacum, which are rich in oil, for industrial purposes, e.g. in the paint industr

Zero-inflated hierarchical models for faecal egg counts to assess anthelmintic efficacy

The prevalence of anthelmintic resistance has increased in recent years, as a result of the extensive use of anthelmintic drugs to reduce the infection of parasitic worms in livestock. In order to detect the resistance, the number of parasite eggs in animal faeces is counted. Typically a subsample of the diluted faeces is examined, and the mean egg counts from both untreated and treated animals are compared. However, the conventional method ignores the variabilities introduced by the counting process and by different infection levels across animals. In addition, there can be extra zero counts, which arise as a result of the unexposed animals in an infected population or animals. In this paper, we propose the zero-inflated Bayesian hierarchical models to estimate the reduction in faecal egg counts. The simulation study compares the Bayesian models with the conventional faecal egg count reduction test and other methods such as bootstrap and quasi-Poisson regression. The results show the Bayesian models are more robust and they perform well in terms of both the bias and the coverage. We further illustrate the advantages of our proposed model using a case study about the anthelmintic resistance in Swedish sheep flocks