Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.Comment: 12 pages. Statistical Paper

Derivative Computations and Robust Standard Errors for Linear Mixed Effects Models in lme4

While robust standard errors and related facilities are available in R for many types of statistical models, the facilities are notably lacking for models estimated via lme4. This is because the necessary statistical output, including the Hessian and casewise gradient of random effect parameters, is not immediately available from lme4 and is not trivial to obtain. In this article, we supply and describe two new functions to obtain this output from Gaussian mixed models: estfun.lmerMod() and vcov.full.lmerMod(). We discuss the theoretical results implemented in the code, focusing on calculation of robust standard errors via package sandwich. We also use the Sleepstudy data to illustrate the code and compare it to a benchmark from package lavaan.Comment: Accepted at Journal of Statistical Softwar

Joint constraints on galaxy bias and σ8\sigma_8 through the N-pdf of the galaxy number density

We present a full description of the N-probability density function of the galaxy number density fluctuations. This N-pdf is given in terms, on the one hand, of the cold dark matter correlations and, on the other hand, of the galaxy bias parameter. The method relies on the assumption commonly adopted that the dark matter density fluctuations follow a local non-linear transformation of the initial energy density perturbations. The N-pdf of the galaxy number density fluctuations allows for an optimal estimation of the bias parameter (e.g., via maximum-likelihood estimation, or Bayesian inference if there exists any a priori information on the bias parameter), and of those parameters defining the dark matter correlations, in particular its amplitude ($\sigma_8$). It also provides the proper framework to perform model selection between two competitive hypotheses. The parameters estimation capabilities of the N-pdf are proved by SDSS-like simulations (both ideal log-normal simulations and mocks obtained from Las Damas simulations), showing that our estimator is unbiased. We apply our formalism to the 7th release of the SDSS main sample (for a volume-limited subset with absolute magnitudes $M_r \leq -20$). We obtain $\hat{b} = 1.193 \pm 0.074$ and $\hat{\sigma_8} = 0.862 \pm 0.080$, for galaxy number density fluctuations in cells of a size of $30h^{-1}$Mpc. Different model selection criteria show that galaxy biasing is clearly favoured.Comment: 25 pages, 9 figures, 2 tables. v2: Substantial revision, adding the joint constraints with \sigma_8 and testing with Las Damas mocks. Matches version accepted for publication in JCA

Fisher Lecture: Dimension Reduction in Regression

Beginning with a discussion of R. A. Fisher's early written remarks that relate to dimension reduction, this article revisits principal components as a reductive method in regression, develops several model-based extensions and ends with descriptions of general approaches to model-based and model-free dimension reduction in regression. It is argued that the role for principal components and related methodology may be broader than previously seen and that the common practice of conditioning on observed values of the predictors may unnecessarily limit the choice of regression methodology.Comment: This paper commented in: [arXiv:0708.3776], [arXiv:0708.3777], [arXiv:0708.3779]. Rejoinder in [arXiv:0708.3781]. Published at http://dx.doi.org/10.1214/088342306000000682 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org