Archive for the ‘Cross-Cultural’ Category.

#### [Book] The Elements of Statistical Learning, 2nd Ed.

This was written more than a year ago, and I forgot to post it.
Continue reading ‘[Book] The Elements of Statistical Learning, 2nd Ed.’ »

#### Galileo’s Revenge

The Vatican adopts the FITS standard. Yes, really.

(via /.)

#### A short note on Probability for astronomers

I often feel irksome whenever I see a function being normalized over a feasible parameter space and it being used as a probability density function (pdf) for further statistical inference. In order to be a suitable pdf, normalization has to be done over a measurable space not over a feasible space. Such practice often yields biased best fits (biased estimators) and improper error bars. On the other hand, validating a measurable space under physics seems complicated. To be precise, we often lost in translation. Continue reading ‘A short note on Probability for astronomers’ »

#### From Terence’s stuff: You want proof?

Please, IMS Bulletin, v.38 (10) check p.11 of this pdf file for the whole article. Continue reading ‘From Terence’s stuff: You want proof?’ »

#### arxiv list

When I begin to subscribe arXiv/astro-ph and arXiv/stat, although only for a year I listed astro-ph papers featuring relatively advanced statistics, I also kept more papers relevant to astrostatistics beyond astro-ph or introducing hot topics in statistics and computer science for astronomical data applications. While creating my own arXiv as follows, I had a hope to write up short introductions of statistics that are unlikely known to most of astronomers (like my MADS) and matching subjects/targets in astronomy. I thought such effort could spawn new collaborations or could expand understanding of statistics among astronomers (see Magic Crystal). Well, I couldn’t catch up the growth rate and it’s about time to terminate the hope. However, I thought some papers can be useful to some slog subscribers. I hope they do. Continue reading ‘arxiv list’ »

#### some python modules

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’ »

#### Quotes from Common Errors in Statistics

by P.I.Good and J.W.Hardin. Publisher’s website

My astronomer neighbor mentioned this book a while ago and quite later I found intriguing quotes. Continue reading ‘Quotes from Common Errors in Statistics’ »

#### The chance that A has nukes is p%

I watched a movie in which one of the characters said, “country A has nukes with 80% chance” (perhaps, not 80% but it was a high percentage). One of the statements in that episode is that people will not eat lettuce only if the 1% chance of e coli is reported, even lower. Therefore, with such a high percentage of having nukes, it is right to send troops to A. This episode immediately brought me a thought about astronomers’ null hypothesis probability and their ways of concluding chi-square goodness of fit tests, likelihood ratio tests, or F-tests.

First of all, I’d like to ask how you would like to estimate the chance of having nukes in a country? What this 80% implies here? But, before getting to the question, I’d like to discuss computing the chance of e coli infection, first. Continue reading ‘The chance that A has nukes is p%’ »

#### [ArXiv] classifying spectra

[arXiv:stat.ME:0910.2585]
Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications
by Murphy, Dean, and Raftery

Classifying or clustering (or semi supervised learning) spectra is a very challenging problem from collecting statistical-analysis-ready data to reducing the dimensionality without sacrificing complex information in each spectrum. Not only how to estimate spiky (not differentiable) curves via statistically well defined procedures of estimating equations but also how to transform data that match the regularity conditions in statistics is challenging.
Continue reading ‘[ArXiv] classifying spectra’ »

#### Scatter plots and ANCOVA

Astronomers rely on scatter plots to illustrate correlations and trends among many pairs of variables more than any scientists[1]. Pages of scatter plots with regression lines are often found from which the slope of regression line and errors bars are indicators of degrees of correlation. Sometimes, too many of such scatter plots makes me think that, overall, resources for drawing nice scatter plots and papers where those plots are printed are wasted. Why not just compute correlation coefficients and its error and publicize the processed data for computing correlations, not the full data, so that others can verify the computation results for the sake of validation? A couple of scatter plots are fine but when I see dozens of them, I lost my focus. This is another cultural difference. Continue reading ‘Scatter plots and ANCOVA’ »

1. This is not an assuring absolute statement but a personal impression after reading articles of various fields in addition to astronomy. My readings of other fields tell that many rely on correlation statistics but less scatter plots by adding straight lines going through data sets for the purpose of imposing relationships within variable pairs[]

#### [MADS] logistic regression

Although a bit of time has elapsed since my post space weather, saying that logistic regression is used for prediction, it looks like still true that logistic regression is rarely used in astronomy. Otherwise, it could have been used for the similar purpose not under the same statistical jargon but under the Bayesian modeling procedures. Continue reading ‘[MADS] logistic regression’ »

#### [MADS] Kalman Filter

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’ »

#### data analysis system and its documentation

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.[]

#### To Become a Good Astronomer

By accident, a piece of paper was found from my old text book. I have no idea who wrote this, nor how old it is. Too old to be obsolete? But it has general description to become a good person and scientist Continue reading ‘To Become a Good Astronomer’ »