467 research outputs found
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
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