Bootstrap joint prediction regions

Many statistical applications require the forecast of a random variable of interest over several periods into the future. The sequence of individual forecasts, one period at a time, is called a path forecast, where the term path refers to the sequence of individual future realizations of the random variable. The problem of constructing a corresponding joint prediction region has been rather neglected in the literature so far: such a region is supposed to contain the entire future path with a prespecified probability. We develop bootstrap methods to construct joint prediction regions. The resulting regions are proven to be asymptotically consistent under a mild high-level assumption. We compare the finitesample performance of our joint prediction regions to some previous proposals via Monte Carlo simulations. An empirical application to a real data set is also provided.Generalized error rates, path forecast, simultaneous prediction intervals

World-wide surveillance of influenza

Summary: Influenza epidemics and pandemics resulting in excess mortality are due to various Influenza viruses, in which through the accumulation of mutations the structure changes. A world-wide surveillance has been set up for early detection of new influenza virus strains and of epidemics or pandemics resulting thereof. Basing on such data the World Health Organisation (WHO) issues recommendations to Public Health authorities on the most efficient means for preventio

Controlling the danger of false discoveries in estimating multiple treatment effects

I expose the risk of false discoveries in the context of multiple treatment effects. A false discovery is a nonexistent effect that is falsely labeled as statistically significant by its individual t-value. Labeling nonexistent effects as statistically significant has wide-ranging academic and policy-related implications, like costly false conclusions from policy evaluations. I eexamine an empirical labor market model by using state-of-the art multiple testing methods and I provide simulation evidence. By merely using individual t-values at conventional significance levels, the risk of labeling probably nonexistent treatment effects as statistically significant is unacceptably high. Individual t-values even label a number of treatment effects as significant, whereas multiple testing indicates false discoveries in these cases. Tests of a joint null hypothesis such as the well-known F-test control the risk of false discoveries only to a limited extent and do not optimally allow for rejecting individual hypotheses. Multiple testing methods control the risk of false discoveries in general while allowing for individual decisions in the sense of rejecting individual hypotheses

Evaluation of the combined measurement uncertainty in isotope dilution by MC-ICP-MS

The combination of metrological weighing, the measurement of isotope amount ratios by a multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS) and the use of high-purity reference materials are the cornerstones to achieve improved results for the amount content of lead in wine by the reversed isotope dilution technique. Isotope dilution mass spectrometry (IDMS) and reversed IDMS have the potential to be a so-called primary method, with which close comparability and well-stated combined measurement uncertainties can be obtained. This work describes the detailed uncertainty budget determination using the ISO-GUM approach. The traces of lead in wine were separated from the matrix by ion exchange chromatography after HNO3/H2O2 microwave digestion. The thallium isotope amount ratio (n(205Tl)/n(203Tl)) was used to correct for mass discrimination using an exponential model approach. The corrected lead isotope amount ratio n(206Pb)/n(208Pb) for the isotopic standard SRM981 measured in our laboratory was compared with ratio values considered to be the least uncertain. The result has been compared in a so-called pilot study "lead in wine" organised by the CCQM (Comité Consultatif pour la Quantité de Matière, BIPM, Paris; the highest measurement authority for analytical chemical measurements). The result for the lead amount content k(Pb) and the corresponding expanded uncertainty U given by our laboratory was: k(Pb)=1.329×10−10molg−1 (amount content of lead in wine) U[k(Pb)]=1.0×10−12molg−1 (expanded uncertainty U=k×u c , k=2) The uncertainty of the main influence parameter of the combined measurement uncertainty was determined to be the isotope amount ratio R 206,B of the blend between the enriched spike and the sampl