Better estimates from binned income data: Interpolated CDFs and mean-matching

Researchers often estimate income statistics from summaries that report the number of incomes in bins such as \$0-10,000, \$10,001-20,000,...,\$200,000+. Some analysts assign incomes to bin midpoints, but this treats income as discrete. Other analysts fit a continuous parametric distribution, but the distribution may not fit well. We fit nonparametric continuous distributions that reproduce the bin counts perfectly by interpolating the cumulative distribution function (CDF). We also show how both midpoints and interpolated CDFs can be constrained to reproduce the mean of income when it is known. We compare the methods' accuracy in estimating the Gini coefficients of all 3,221 US counties. Fitting parametric distributions is very slow. Fitting interpolated CDFs is much faster and slightly more accurate. Both interpolated CDFs and midpoints give dramatically better estimates if constrained to match a known mean. We have implemented interpolated CDFs in the binsmooth package for R. We have implemented the midpoint method in the rpme command for Stata. Both implementations can be constrained to match a known mean.Comment: 20 pages (including Appendix), 3 tables, 2 figures (+2 in Appendix

A Bayesian Approach to Deriving Ages of Individual Field White Dwarfs

We apply a self-consistent and robust Bayesian statistical approach to determining the ages, distances, and ZAMS masses of 28 field DA white dwarfs with ages of approximately 4 to 8 Gyrs. Our technique requires only quality optical and near-IR photometry to derive ages with < 15% uncertainties, generally with little sensitivity to our choice of modern initial-final mass relation. We find that age, distance, and ZAMS mass are correlated in a manner that is too complex to be captured by traditional error propagation techniques. We further find that the posterior distributions of age are often asymmetric, indicating that the standard approach to deriving WD ages can yield misleading results.Comment: 9 pages, 10 figures, accepted for publication in Ap

Experiences of WFPC-2 Photometry and PSF Modelling

The wide-field chips in WFPC-2 have ~0.0996 arcsec pixels, which is larger than the FWHM of the point spread function (PSF). This poor sampling of WFPC-2 images, means that simple stellar aperture photometry is competitive with profile fitting for moderately crowded fields. One of the problems which must be addressed with profile fitting photometry is that of allowing for variations of the PSF as a function of position on the image. The equivalent problem for aperture photometry is obtaining aperture corrections as a function of position. The aperture correction must be added to each aperture magnitude in order to account for the flux from the star falling outside the aperture. If the aperture used is large, and with WFPC-2 this means about 1 arcsec in diameter, then the aperture corrections will be both small and fairly constant. However, to obtain better signal-to-noise especially in crowded fields, and hence take full advantage of the superb resolution of HST, it is desirable to use smaller apertures

Transcription factor search for a DNA promoter in a three-states model

To ensure fast gene activation, Transcription Factors (TF) use a mechanism known as facilitated diffusion to find their DNA promoter site. Here we analyze such a process where a TF alternates between 3D and 1D diffusion. In the latter (TF bound to the DNA), the TF further switches between a fast translocation state dominated by interaction with the DNA backbone, and a slow examination state where interaction with DNA base pairs is predominant. We derive a new formula for the mean search time, and show that it is faster and less sensitive to the binding energy fluctuations compared to the case of a single sliding state. We find that for an optimal search, the time spent bound to the DNA is larger compared to the 3D time in the nucleus, in agreement with recent experimental data. Our results further suggest that modifying switching via phosphorylation or methylation of the TF or the DNA can efficiently regulate transcription.Comment: 4 pages, 3 figure

The White Dwarf Age of NGC 2477

We present deep photometric observations of the open cluster NGC 2477 using HST/WFPC2. By identifying seven cluster white dwarf candidates, we present an analysis of the white dwarf age of this cluster, using both the traditional method of fitting isochrones to the white dwarf cooling sequence, and by employing a new Bayesian statistical technique that has been developed by our group. This new method performs an objective, simultaneous model fit of the cluster and stellar parameters (namely age, metallicity, distance, reddening, as well as individual stellar masses, mass ratios, and cluster membership) to the photometry. Based on this analysis, we measure a white dwarf age of 1.035 +/- 0.054 +/- 0.087 Gyr (uncertainties represent the goodness of model fits and discrepancy among models, respectively), in good agreement with the cluster's main sequence turnoff age. This work is part of our ongoing work to calibrate main sequence turnoff and white dwarf ages using open clusters, and to improve the precision of cluster ages to the ~5% level.Comment: 24 pages, 8 figures, accepted Ap

(Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks

New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic analysis are no longer practical. New tools are required that are capable of extracting quickly and with reasonable accuracy important basic stellar parameters coded in the spectra. Recent analyses of Artificial Neural Networks (ANNs) applied to the classification of astronomical spectra have demonstrated the ability of this concept to derive estimates of temperature and luminosity. We have adapted the back-propagation ANN technique developed by von Hippel et al. (1994) to predict effective temperatures, gravities and overall metallicities from spectra with resolving power ~ 2000 and low signal-to-noise ratio. We show that ANN techniques are very effective in executing a three-parameter (Teff,log g,[Fe/H]) stellar classification. The preliminary results show that the technique is even capable of identifying outliers from the training sample.Comment: 6 pages, 3 figures (5 files); to appear in the proceedings of the 11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun, held on Tenerife (Spain), October 1999; also available at http://hebe.as.utexas.ed

Nonspecific Protein-DNA Binding Is Widespread in the Yeast Genome

Recent genome-wide measurements of binding preferences of ~200 transcription regulators in the vicinity of transcription start sites in yeast, have provided a unique insight into the cis- regulatory code of a eukaryotic genome (Venters et al., Mol. Cell 41, 480 (2011)). Here, we show that nonspecific transcription factor (TF)-DNA binding significantly influences binding preferences of the majority of transcription regulators in promoter regions of the yeast genome. We show that promoters of SAGA-dominated and TFIID-dominated genes can be statistically distinguished based on the landscape of nonspecific protein-DNA binding free energy. In particular, we predict that promoters of SAGA-dominated genes possess wider regions of reduced free energy compared to promoters of TFIID-dominated genes. We also show that specific and nonspecific TF-DNA binding are functionally linked and cooperatively influence gene expression in yeast. Our results suggest that nonspecific TF-DNA binding is intrinsically encoded into the yeast genome, and it may play a more important role in transcriptional regulation than previously thought