As a part of exploring spatial distribution of particles/objects, not to approximate via Poisson process or Gaussian process (parametric), nor to impose hypotheses such as homogenous, isotropic, or uniform, various nonparametric methods somewhat dragged my attention for data exploration and preliminary analysis. Among various nonparametric methods, the one that I fell in love with is tessellation (state space approaches are excluded here). Computational speed wise, I believe tessellation is faster than kernel density estimation to estimate level sets for multivariate data. Furthermore, conceptually constructing polygons from tessellation is intuitively simple. However, coding and improving algorithms is beyond statistical research (check books titled or key-worded partially by computational geometry). Good news is that for computation and getting results, there are some freely available softwares, packages, and modules in various forms. Continue reading ‘[ArXiv] Voronoi Tessellations’ »
Archive for the ‘Galaxies’ Category.
A continuation from my posting, titled circumspect frequentist.
Title: Statistical Models: Theory and Practice (click for the publisher’s website)
My one line review, rather a comment several months ago was
Bias in asymptotic standard errors is not a familiar topic for astronomers
and I don’t understand why I wrote it but I think I came up this comment owing to my pursuit of modeling measurement errors occurring in astronomical researches. Continue reading ‘A book by David Freedman’ »
We have seen the word “bipartisan” often during the election and during the on-going recession period. Sometimes, I think that the bipartisanship is not driven by politicians but it’s driven by media, commentator, and interpreters. Continue reading ‘Bipartisanship’ »
Since I learned Hubble’s tuning fork for the first time, I wanted to do classification (semi-supervised learning seems more suitable) galaxies based on their features (colors and spectra), instead of labor intensive human eye classification. Ironically, at that time I didn’t know there is a field of computer science called machine learning nor statistics which do such studies. Upon switching to statistics with a hope of understanding statistical packages implemented in IRAF and IDL, and learning better the contents of Numerical Recipes and Bevington’s book, the ignorance was not the enemy, but the accessibility of data was. Continue reading ‘[ArXiv] 5th week, Apr. 2008’ »
Grand statistical challenges seem to be all the rage nowadays. Following on the heels of the Banff Challenge (which dealt with figuring out how to set the bounds for the signal intensity that would result from the Higgs boson) comes the GREAT08 Challenge (arxiv/0802.1214) to deal with one of the major issues in observational Cosmology, the effect of dark matter. As Douglas Applegate puts it: Continue reading ‘The GREAT08 Challenge’ »
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description by M. Huertas-Company et al.
Machine learning and statistical learning become more and more popular in astronomy. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are hardly missed when classifying on massive survey data is the objective. The authors provide a gentle tutorial on SVM for galactic morphological classification. Their source code GALSVM is linked for the interested readers.
Continue reading ‘[ArXiv] SVM and galaxy morphological classification, Sept. 10, 2007’ »
Connecting GRBs and galaxies: the probability of chance coincidence by Cobb and Bailyn
Without an optical afterglow, a galaxy within the 2 arc second error region of a GRB x-ray afterglow is identified as a host galaxy; however confusion can rise due to the facts that 1. the edge of a galaxy is diffused, 2. multiple sources could exist within 2 arc second error region, 3.the distance between the galaxy and the x-ray afterglow is measured by projection, and 4. lensing causes increase of brightness and position shifts. In this paper, the authors “investigated the fields of 72 GRBs in order to examine the general issue of associations between GRBs and host galaxies.”
Continue reading ‘[ArXiv] GRB host galaxies, Aug. 10, 2007’ »
Since I began to subscribe arxiv/astro-ph abstracts, from an astrostatistical point of view, one of the most frequent topics has been photometric redshifts. This photometric redshift has been a popular topic as the catalog of remote photometric object observation multiplies its volume and sky survey projects in multiple bands lead to virtual observatories (VO – will discuss in the later posting). Just searching by photometric redshifts in google scholar and arxiv.org provides more than 2000 articles since 2000.
Continue reading ‘Photometric Redshifts’ »
One of the papers from arxiv/astro-ph discusses kernel regression and model selection to determine photometric redshifts astro-ph/0706.2704. This paper presents their studies on choosing bandwidth of kernels via 10 fold cross-validation, choosing appropriate models from various combination of input parameters through estimating root mean square error and AIC, and evaluating their kernel regression to other regression and classification methods with root mean square errors from literature survey. They made a conclusion of flexibility in kernel regression particularly for data at high z.
Continue reading ‘[ArXiv] Kernel Regression, June 20, 2007’ »