Significance Regression: Robust Regression for Collinear Data

This paper examines robust linear multivariable regression from collinear data. A brief review of M-estimators discusses the strengths of this approach for tolerating outliers and/or perturbations in the error distributions. The review reveals that M-estimation may be unreliable if the data exhibit collinearity. Next, significance regression (SR) is discussed. SR is a successful method for treating collinearity but is not robust. A new significance regression algorithm for the weighted-least-squares error criterion (SR-WLS) is developed. Using the weights computed via M-estimation with the SR-WLS algorithm yields an effective method that robustly mollifies collinearity problems. Numerical examples illustrate the main points

Non-detection of a statistically anisotropic power spectrum in large-scale structure

We search a sample of photometric luminous red galaxies (LRGs) measured by the Sloan Digital Sky Survey (SDSS) for a quadrupolar anisotropy in the primordial power spectrum, in which P(\vec{k}) is an isotropic power spectrum P(k) multiplied by a quadrupolar modulation pattern. We first place limits on the 5 coefficients of a general quadrupole anisotropy. We also consider axisymmetric quadrupoles of the form P(\vec{k}) = P(k){1 + g_*[(\hat{k}\cdot\hat{n})^2-1/3]} where \hat{n} is the axis of the anisotropy. When we force the symmetry axis \hat{n} to be in the direction (l,b)=(94 degrees,26 degrees) identified in the recent Groeneboom et al. analysis of the cosmic microwave background, we find g_*=0.006+/-0.036 (1 sigma). With uniform priors on \hat{n} and g_* we find that -0.41<g_*<+0.38 with 95% probability, with the wide range due mainly to the large uncertainty of asymmetries aligned with the Galactic Plane. In none of these three analyses do we detect evidence for quadrupolar power anisotropy in large scale structure.Comment: 23 pages; 10 figures; 3 tables; replaced with version published in JCAP (added discussion of scale-varying quadrupolar anisotropy

Testing for selectivity bias in panel data models

Estimation;Panel Data