Field #3 of the Palomar-Groningen Survey II. Near-infrared photometry of semiregular variables

Near-infrared photometry (JHKL'M) was obtained for 78 semiregular variables (SRVs) in field #3 of the Palomar-Groningen survey (PG3, l=0, b=-10). Together with a sample of Miras in this field a comparison is made with a sample of field SRVs and Miras. The PG3 SRVs form a sequence (period-luminosity & period-colour) with the PG3 Miras, in which the SRVs are the short period extension to the Miras. The field and PG3 Miras follow the same P/(J--K)o relation, while this is not the case for the field and PG3 SRVs. Both the PG3 SRVs and Miras follow the SgrI period-luminosity relation adopted from Glass et al. (1995, MNRAS 273, 383). They are likely pulsating in the fundamental mode and have metallicities spanning the range from intermediate to approximately solar.Comment: 14 pages LaTeX (2 tables, 8 figures), to appear in A&A 338 (1998); minor modifications in tex

Robust compositional data analysis

Many practical data sets contain outliers or other forms of data inhomogeneities. Robust statistics offers concepts how to deal with these situations where the data do not follow strict model assumptions. These concepts are designed for the usual Euclidean space, and they can be easily applied to compositional data if they are represented in this space as well. It turns out that the isometric logratio (ilr) transformation is best suitable in the context of robust estimation. Depending on the method applied, an interpretation of result is usually done in a back-transformed space

Modelling the atmosphere of the carbon-rich Mira RU Vir

Context. We study the atmosphere of the carbon-rich Mira RU Vir using the mid-infrared high spatial resolution interferometric observations from VLTI/MIDI. Aims. The aim of this work is to analyse the atmosphere of the carbon-rich Mira RU Vir, with state of the art models, in this way deepening the knowledge of the dynamic processes at work in carbon-rich Miras. Methods. We compare spectro-photometric and interferometric measurements of this carbon-rich Mira AGB star, with the predictions of different kinds of modelling approaches (hydrostatic model atmospheres plus MOD-More Of Dusty, self-consistent dynamic model atmospheres). A geometric model fitting tool is used for a first interpretation of the interferometric data. Results. The results show that a joint use of different kind of observations (photometry, spectroscopy, interferometry) is essential to shed light on the structure of the atmosphere of a carbon-rich Mira. The dynamic model atmospheres fit well the ISO spectrum in the wavelength range {\lambda} = [2.9, 25.0] {\mu}m. Nevertheless, a discrepancy is noticeable both in the SED (visible), and in the visibilities (shape and level). A possible explanation are intra-/inter-cycle variations in the dynamic model atmospheres as well as in the observations. The presence of a companion star and/or a disk or a decrease of mass loss within the last few hundred years cannot be excluded but are considered unlikely.Comment: 15 pages. Accepted in A&

Analysis of compositional data using robust methods. The R-package robCompositons

The free and open-source programming language and software environment R (R Development Core Team, 2010) is currently both, the most widely used and most popular software for statistics and data analysis. In addition, R becomes quite popular as a (programming) language, ranked currently (February 2011) on place 25 at the TIOBE Programming Community Index (e.g., Matlab: 29, SAS: 30, see http://www.tiobe.com). The basic R environment can be downloaded from the comprehensive R archive network (http://cran.rproject.org). R is enhanceable via packages which consist of code and structured standard documentation including code application examples and possible further documents (so called vignettes) showing further applications of the packages. Two contributed packages for compositional data analysis comes with R, version 2.12.1.: the package compositions (van den Boogaart et al., 2010) and the package robCompositions (Templ et al., 2011). Package compositions provides functions for the consistent analysis of compositional data and positive numbers in the way proposed originally by John Aitchison (see van den Boogaart et al., 2010). In addition to the basic functionality and estimation procedures in package compositions, package robCompositions provides tools for a (classical) and robust multivariate statistical analysis of compositional data together with corresponding graphical tools. In addition, several data sets are provided as well as useful utility functions

Classical and robust imputation of missing values for compositional data using balances

Classical and Robust Imputation of Missing Values for Compositional Data using Balance

Placebo or Panacea: The FDA's Rejection of ImClone's Erbitux Licensing Application

This paper draws upon the media reports, congressional hearing testimony, and company press releases to recount events surrounding the FDA’s refusal to issue a license to ImClone’s cancer drug Erbitux, late in 2001. Erbitux was granted fast-track status by FDA, and was evaluated under the agency’s accelerated approval process. Despite hype about the drug’s effectiveness in fighting certain types of cancer, the FDA found numerous and considerable problems with the licensing application, and in particular with the conduct and documentation of the main registration trial. The paper discusses the possibility that ImClone’s public statements may have misled investors, and the ability of the FDA and the SEC to oversee these disclosures. Finally, recent changes in the FDA approval process are addressed, as well as the current state of ImClone’s continuing attempts to gain licensing approval for Erbitux

Simplicial principal component analysis for density functions in Bayes spaces

Probability density functions are frequently used to characterize the distributional properties of large-scale database systems. As functional compositions, densities primarily carry relative information. As such, standard methods of functional data analysis (FDA) are not appropriate for their statistical processing. The specific features of density functions are accounted for in Bayes spaces, which result from the generalization to the infinite dimensional setting of the Aitchison geometry for compositional data. The aim is to build up a concise methodology for functional principal component analysis of densities. A simplicial functional principal component analysis (SFPCA) is proposed, based on the geometry of the Bayes space B2 of functional compositions. SFPCA is performed by exploiting the centred log-ratio transform, an isometric isomorphism between B2 and L2 which enables one to resort to standard FDA tools. The advantages of the proposed approach with respect to existing techniques are demonstrated using simulated data and a real-world example of population pyramids in Upper Austria