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?’ »
Archive for the ‘Frequentist’ Category.
by Emanuel Parzen in Statistical Science 2004, Vol 19(4), pp.652-662 JSTOR
I teach that statistics (done the quantile way) can be simultaneously frequentist and Bayesian, confidence intervals and credible intervals, parametric and nonparametric, continuous and discrete data. My first step in data modeling is identification of parametric models; if they do not fit, we provide nonparametric models for fitting and simulating the data. The practice of statistics, and the modeling (mining) of data, can be elegant and provide intellectual and sensual pleasure. Fitting distributions to data is an important industry in which statisticians are not yet vendors. We believe that unifications of statistical methods can enable us to advertise, “What is your question? Statisticians have answers!”
I couldn’t help liking this paragraph because of its bitter-sweetness. I hope you appreciate it as much as I did.
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%’ »
When it comes to applying statistics for measuring goodness-of-fit, the Pearson χ2 test is the dominant player in a race and the Kolmogorov-Smirnoff test statistic trails far behind. Although it seems almost invisible in this race, there are more various non-parametric statistics for testing goodness-of-fit and for comparing the sampling distribution to a reference distribution as legitimate race participants trained by many statisticians. Listing their names probably useful to some astronomers when they find the underlying assumptions for the χ2 test do not match the data. Perhaps, some astronomers want to try other nonparametric test statistics other than the K-S test. I’ve seen other test statistics in astronomical journals from time to time. Depending on data and statistical properties, one test statistic could work better than the other; therefore, it’s worthwhile to keep the variety in one’s mind that there are other tests beyond the χ2 test goodness-of-fit test statistic. Continue reading ‘Goodness-of-fit tests’ »
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’ »
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’ »
Ah ha~ Once I questioned, “what is systematic error?” (see [Q] systematic error.) Thanks to L. Lyons’ work discussed in [ArXiv] Particle Physics, I found this paper, titled Systematic Errors describing the concept and statistical inference related to systematic errors in the field of particle physics. It, gladly, shares lots of similarity with high energy astrophysics. Continue reading ‘systematic errors’ »
Open Statistical Issues in Particle Physics by Louis Lyons
My recollection of meeting Prof. L. Lyons was that he is very kind and listening. I was delighted to see his introductory article about particle physics and its statistical challenges from an [arxiv:stat] email subscription. Continue reading ‘[ArXiv] Particle Physics’ »
Student’s t-distribution is somewhat underrepresented in the astronomical community. Having an article with nice stories, it looks to me the best way to introduce the t distribution. This article describing historic anecdotes about monumental statistical developments occurred about 100 years ago.
Guinness, Gosset, Fisher, and Small Samples by Joan Fisher Box
Source: Statist. Sci. Volume 2, Number 1 (1987), 45-52.
No time for reading the whole article? I hope you have a few minutes to read following quotes, which are quite enchanting to me. Continue reading ‘Guinness, Gosset, Fisher, and Small Samples’ »
I wonder what Fisher, Neyman, and Pearson would say if they see “Technique” after “Likelihood Ratio” instead of “Test.” A presenter’s saying “Likelihood Ratio Technique” for source identification, I couldn’t resist checking it out not to offend founding fathers of the likelihood principle in statistics since “Technique” sounded derogatory to be attached with “Likelihood” to my ears. I thank, above all, the speaker who kindly gave me the reference about this likelihood ratio technique. Continue reading ‘Likelihood Ratio Technique’ »
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’ »
Almost two year long scrutinizing some publications by astronomers gave me enough impression that astronomers live in the Gaussian world. You are likely to object this statement by saying that astronomers know and use Poisson, binomial, Pareto (power laws), Weibull, exponential, Laplace (Cauchy), Gamma, and some other distributions. This is true. I witness that these distributions are referred in many publications; however, when it comes to obtaining “BEST FIT estimates for the parameters of interest” and “their ERROR (BARS)”, suddenly everything goes back to the Gaussian world.
Borel Cantelli Lemma (from Planet Math): because of mathematical symbols, a link was made but any probability books have the lemma with proofs and descriptions.
- It is a bit disappointing fact that not many mention the t distribution, even though less than 30 observations are available.[↩]
- To stay off this Gaussian world, some astronomers rely on Bayesian statistics and explicitly say that it is the only escape, which is sometimes true and sometimes not – I personally weigh more that Bayesians are not always more robust than frequentist methods as opposed to astronomers’ discussion about robust methods.[↩]
There will be a special session at the 213th AAS meeting on meaning from surveys and population studies (SPS). Until then, it might be useful to pull out some interesting and relevant papers and questions/challenges as a preliminary to the meeting. I will not list astronomical catalogs and surveys only, which are literally countless these days but will bring out some if they change the way how science is performed with a description of the catalog (the best example would be SDSS, Sloan Digital Sky Survey, to my knowledge). Continue reading ‘[SPS] Testing Completeness’ »
Is there an objective method to combine measurements of the same quantity obtained with different instruments?
Suppose you have a set of N1 measurements obtained with one detector, and another set of N2 measurements obtained with a second detector. And let’s say you wanted something as simple as an estimate of the mean of the quantity (say the intensity) being measured. Let us further stipulate that the measurement errors of each of the points is similar in magnitude and neither instrument displays any odd behavior. How does one combine the two datasets without appealing to subjective biases about the reliability or otherwise of the two instruments? Continue reading ‘[Q] Objectivity and Frequentist Statistics’ »