The AstroStat Slog

For general comments.

The Solar Dynamics Observatory, which promises a flood of data on the Sun, was launched today from Cape Kennedy.

Because of blogging and projects I worked on, I happened to collect quite many publications in Astronomy. The collection is biased toward my personal interests. However, these authors discussed statistics in a wide range. So, I felt my astronomical bibliography can be useful to slog audience. Some areas could match your interests. Or your own name can be found. Continue reading ‘astronomy bibliography’ »

I was told to stay away from python and I’ve obeyed the order sincerely. However, I collected the following stuffs several months back at the instance of hearing about import inference and I hate to see them getting obsolete. At that time, collecting these modules and getting through them could help me complete the first step toward the quest Learning Python (the first posting of this slog). Continue reading ‘some python modules’ »

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’ »

From SINGS (Spitzer Infrared Nearby Galaxies Survey): Isn’t it a beautiful Hubble tuning fork? Continue reading ‘SINGS’ »

The 2009 Physics Nobel is shared (along with Charles Kao, who is cited for suggesting optic fibers) by Willard Boyle and George Smith, inventors of the Charge-coupled Device.

The CCD, of course, is the workhorse of modern Astronomy. I cannot even imagine how things would be without it.
Continue reading ‘Boyle & Smith (1969)’ »

I decide to discuss Kalman Filter a while ago for the slog after finding out that this popular methodology is rather underrepresented in astronomy. However, it is not completely missing from ADS. I see that the fulltext search and all bibliographic source search shows more results. Their use of Kalman filter, though, looked similar to the usage of “genetic algorithms” or “Bayes theorem.” Probably, the broad notion of Kalman filter makes it difficult my finding Kalman Filter applications by its name in astronomy since often wheels are reinvented (algorithms under different names have the same objective). Continue reading ‘[MADS] Kalman Filter’ »

So far, I didn’t complain much related to my “statistician learning astronomy” experience. Instead, I’ve been trying to emphasize how fascinating it is. I hope that more statisticians can join this adventure when statisticians’ insights are on demand more than ever. However, this positivity seems not working so far. In two years of this slog’s life, there’s no posting by a statistician, except one about BEHR. Statisticians are busy and well distracted by other fields with more tangible data sets. Or compared to other fields, too many obstacles and too high barriers exist in astronomy for statisticians to participate. I’d like to talk about these challenges from my ends.^[1] Continue reading ‘data analysis system and its documentation’ »

1. This is quite an overdue posting. Links and associated content can be outdated.[↩]

I happened to observe a surge of principle component analysis (PCA) and independent component analysis (ICA) applications in astronomy. The PCA and ICA is used for separating mixed components with some assumptions. For the PCA, the decomposition happens by the assumption that original sources are orthogonal (uncorrelated) and mixed observations are approximated by multivariate normal distribution. For ICA, the assumptions is sources are independent and not gaussian (it grants one source component to be gaussian, though). Such assumptions allow to set dissimilarity measures and algorithms work toward maximize them. Continue reading ‘[ArXiv] component separation methods’ »

Kriging is the first thing that one learns from a spatial statistics course. If an astronomer sees its definition and application, almost every astronomer will say, “Oh, I know this! It is like the 2pt correlation function!!” At least this was my first impression when I first met kriging.

There are three distinctive subjects in spatial statistics: geostatistics, lattice data analysis, and spatial point pattern analysis. Because of the resemblance between the spatial distribution of observations in coordinates and the notion of spatially random points, spatial statistics in astronomy has leaned more toward the spatial point pattern analysis than the other subjects. In other fields from immunology to forestry to geology whose data are associated spatial coordinates of underlying geometric structures or whose data were sampled from lattices, observations depend on these spatial structures and scientists enjoy various applications from geostatistics and lattice data analysis. Particularly, kriging is the fundamental notion in geostatistics whose application is found many fields. Continue reading ‘[MADS] Kriging’ »

This is a special session at the January 2010 meeting of the AAS. It is scheduled for the afternoon of Thursday, Jan 7, 2-3:30pm.

Abstracts are due Sep 17.

Meeting Justification

We propose to highlight the growing use of ‘non-parametric’ techniques to distill meaningful science from today’s astronomical data. Challenges range from Kuiper objects to cosmology. We have chosen just a few ‘teaching’ examples from this lively interdisciplinary area.

Continue reading ‘Beyond simple models-New methods for complex data’ »

Statistical Resampling Methods are rather unfamiliar among astronomers. Bootstrapping can be an exception but I felt like it’s still unrepresented. Seeing an recent review paper on cross validation from [arXiv] which describes basic notions in theoretical statistics, I couldn’t resist mentioning it here. Cross validation has been used in various statistical fields such as classification, density estimation, model selection, regression, to name a few. Continue reading ‘[ArXiv] Cross Validation’ »

Speaking of XAtlas from my previous post I tried another visualization tool called Parallel Coordinates on these Capella observations and two stars with multiple observations (AL Lac and IM Peg). As discussed in [MADS] Chernoff face, full description of the catalog is found from XAtlas website. The reason for choosing these stars is that among low mass stars, next to Capella (I showed 16), IM PEG (HD 21648, 8 times), and AR Lac (although different phases, 6 times) are most frequently observed. I was curious about which variation, within (statistical variation) and between (Capella, IM Peg, AL Lac), is dominant. How would they look like from the parametric space of High Resolution Grating Spectroscopy from Chandra? Continue reading ‘[MADS] Parallel Coordinates’ »

I’m getting behind these days because of chasing too many rabbits. One of those rabbits is hunting online lectures useful for everyone. Prof. Feynman’s lectures have great reputations but they have been hard to come by. I once listened to a pirate version of his lecture tape with horrible sound quality. Thanks to Bill Gates and Microsoft Research, although it is a belated news, I’m very delighted to say “Feynman lectures are online.” Continue reading ‘News and related stories’ »