University of Wisconsin-Madison

About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.

The eleventh and twelfth data releases of the Sloan Digital Sky Survey: final data from SDSS-III

Abstract: The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 sq. deg of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include measured abundances of 15 different elements for each star. In total, SDSS-III added 2350 sq. deg of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 sq. deg; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5,513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.

The Robust Inference for the Cox Proportional Hazards Model

Abstract: We derive the asymptotic distribution of the maximum partial likelihood estimator β for the vector of regression coefficients β under a possibly misspecified Cox proportional hazards model. As in the parametric setting, this estimator β converges to a well-defined constant vector β*. In addition, the random vector n 1/2(β – β*) is asymptotically normal with mean 0 and with a covariance matrix that can be consistently estimated. The newly proposed robust covariance matrix estimator is similar to the so-called “sandwich” variance estimators that have been extensively studied for parametric cases. For many misspecified Cox models, the asymptotic limit β* or part of it can be interpreted meaningfully. In those circumstances, valid statistical inferences about the corresponding covariate effects can be drawn based on the aforementioned asymptotic theory of β and the related results for the score statistics. Extensive studies demonstrate that the proposed robust tests and interval estimation procedures...

Catastrophic regime shifts in ecosystems: linking theory to observation

Abstract: Occasionally, surprisingly large shifts occur in ecosystems. Theory suggests that such shifts can be attributed to alternative stable states. Verifying this diagnosis is important because it implies a radically different view on management options, and on the potential effects of global change on such ecosystems. For instance, it implies that gradual changes in temperature or other factors might have little effect until a threshold is reached at which a large shift occurs that might be difficult to reverse. Strategies to assess whether alternative stable states are present are now converging in fields as disparate as desertification, limnology, oceanography and climatology. Here, we review emerging ways to link theory to observation, and conclude that although, field observations can provide hints of alternative stable states, experiments and models are essential for a good diagnosis.

Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry.

Abstract: Darwin (1859, pp. 11-14, 143-150) stressed the evolutionary importance of covariation between characters in populations and its "most imperfectly understood" connection with correlated responses to artificial and natural selection. After the turn of the century the discoveries of pervasive pleiotropy and linkage of Mendelian factors revealed the underlying genetic mechanisms. Existing theory on the dynamics of correlated characters has been developed in the limited framework of practical plant and animal breeding. Methods of multivariate analysis, functional analysis and optimality criteria popular among evolutionists, do not account for dynamical constraints imposed by the pattern of genetic variation within populations. Consideration of phenotypic variation often does not suggest any clear mechanism connecting growth patterns or adult variation to interspecific evolution. When there is individual variation in development, no necessary correspondence exists between ontogenetic and adult variation in a population (Cock, 1966, pp. 148-15 1). It is also common for the pattern of adult variation within a species to differ from that at higher taxonomic levels (Simpson, 1953, pp. 25-29). An example which will be investigated here is the brain weight:body weight relationship. At various taxonomic levels, brain and body weights tend to follow the allometric equation