Testing the suitability of polynomial models in errors-in-variables problems

A low-degree polynomial model for a response curve is used commonly in practice. It generally incorporates a linear or quadratic function of the covariate. In this paper we suggest methods for testing the goodness of fit of a general polynomial model when there are errors in the covariates. There, the true covariates are not directly observed, and conventional bootstrap methods for testing are not applicable. We develop a new approach, in which deconvolution methods are used to estimate the distribution of the covariates under the null hypothesis, and a ``wild'' or moment-matching bootstrap argument is employed to estimate the distribution of the experimental errors (distinct from the distribution of the errors in covariates). Most of our attention is directed at the case where the distribution of the errors in covariates is known, although we also discuss methods for estimation and testing when the covariate error distribution is estimated. No assumptions are made about the distribution of experimental error, and, in particular, we depart substantially from conventional parametric models for errors-in-variables problems.Comment: Published in at http://dx.doi.org/10.1214/009053607000000361 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Executive Pay in the Public Sector: The Case of CEOs in UK Universities

We analyse top management public sector pay using a panel data of university Vice Chancellors (VC) in UK. We assess how institutional performance, hierarchical effects, and personal characteristics determine VC pay. VC personal data covers personal details, qualifications and career history, which let us distinguish between internal promotions and hires from outside academia. We use the results of three Research Assessment Exercises as academic performance indicators, and university financial positions as measures of sound executive management. We analysed the importance of university salary structure and how they affect VC pay. Fixed and random institutional effects are also identified and analysed.pay, public sector, CEO, universities

Constraining Scatter in the Stellar Mass--Halo Mass Relation for Haloes Less Massive than the Milky Way

Most galaxies are hosted by massive, invisible dark matter haloes, yet little is known about the scatter in the stellar mass--halo mass relation for galaxies with host halo masses $M_{h}\le 10^{11}M_{\odot}$. Using mock catalogues based on dark matter simulations, we find that two observable signatures are sensitive to scatter in the stellar mass--halo mass relation even at these mass scales; i.e., conditional stellar mass functions and velocity distribution functions for neighbouring galaxies. We compute these observables for 179,373 galaxies in the Sloan Digital Sky Survey (SDSS) with stellar masses $M_{\ast} > 10^9 M_{\odot}$ and redshifts 0.01 $< z <$ 0.307. We then compare to mock observations generated from the $\textit{Bolshoi-Planck}$ dark matter simulation for stellar mass--halo mass scatters ranging from 0 to 0.6 dex. The observed results are consistent with simulated results for most values of scatter ($<$0.6 dex), and SDSS statistics are insufficient to provide firm constraints. However, this method could provide much tighter constraints on stellar mass--halo mass scatter in the future if applied to larger data sets, especially the anticipated Dark Energy Spectroscopic Instrument Bright Galaxy Survey. Constraining the value of scatter could have important implications for galaxy formation and evolution.Comment: 11 pages, 1 table, 9 main body figures, 9 appendix figure