#### Yes, please

Andrew Gelman says,

Instead of “confidence interval,” let’s say “uncertainty interval”

Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders

Archive for the ‘Quotes’ Category.

Andrew Gelman says,

Instead of “confidence interval,” let’s say “uncertainty interval”

__This was written more than a year ago, and I forgot to post it.__

Continue reading ‘[Book] The Elements of Statistical Learning, 2nd Ed.’ »

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’ »

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*’ »

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%’ »

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’ »

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 Links and associated content can be outdated.[↩]

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’ »

Is Calculus the ultimate goal of mathematical education? Arthur Benjamin has a slightly subversive suggestion in this TED presentation.

Continue reading ‘Mt. Mathematics’ »

A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution

Vonesch and Unser (2008)

IEEE Trans. Image Proc. vol. 17(4), pp. 539-549

Quoting the authors, I also like to say that __the recovery of the original image from the observed is an ill-posed problem__. They traced the efforts of wavelet regularization in deconvolution back to a few relatively recent publications by astronomers. Therefore, I guess the topic and algorithm of this paper could drag some attentions from astronomers. Continue reading ‘Wavelet-regularized image deconvolution’ »

I was at the SUSY 09 public lecture given by a Nobel laureate, Frank Wilczek of QCD (quantum chromodynamics). As far as I know SUSY is the abbreviation of **SUperSYmetricity** in particle physics. Finding such antimatter(? I’m afraid I read “Angels and Demons” too quickly) will explain the unification theory among electromagnetic, weak, and strong forces and even the gravitation according to the speaker’s graph. I’ll not go into the details of particle physics and the standard model. The reason is too obvious. Instead, I’d like to show this image from wikipedia and to discuss my related questions.

Continue reading ‘how to trace?’ »

My understandings of **“robustness”** from the education in statistics and from communicating with astronomers are hard to find a mutual interest. Can anyone help me to build a robust bridge to get over this abyss? Continue reading ‘Robust Statistics’ »

Almost 100 years ago, A.S. Eddington stated in his book *Stellar Movements* (1914) that

…in calculating the mean error of a series of observations it is preferable to use the simple mean residual irrespective of sign rather than the mean square residual

Such eminent astronomer said already *least absolute deviation* over *chi-square*, if I match *simple mean residual* and *mean square residual* to relevant methodologies, in order. Continue reading ‘a century ago’ »

To my knowledge, **Richard Feynman** is an iconic figure among physicists and astrophysicists. Although I didn’t read every chapter of his lecture series, from other books like* QED*, *Surely You’re Joking, Mr. Feynman!*, *The Pleasure of Finding Things Out*, and some essays, I became and still am fond of him. The way how this famous physicist put things is straight and simple, blowing out the misconception that physics is full of mathematical equations.

Even though most of my memories about his writings are gone – how many people can beat the time and fading memories! – like other rudimentary astronomy and physics stuffs that I used to know, statistics brought up his name above the surface before it sinks completely to the abyss. Continue reading ‘Feynman and Statistics’ »

I was reading Lehmann’s memoir on his friends and colleagues who influence a great deal on establishing his career. I’m happy to know that his meeting Landau, Courant, and Evans led him to be a statistician; otherwise, we, including astronomers, would have had very different textbooks and statistical thinking would have been different. On the other hand, I was surprised to know that he chose statistics over physics due to his experience from Cambridge (UK). I thought becoming a physicist is more preferred than becoming a statistician during the first half of the 20th century. At least I felt that way, probably it’s because more general science books in physics and physics related historic events were well exposed so that I became to think that physicists are more cooler than other type scientists. Continue reading ‘[Book] The Physicists’ »