These are my links for July 29th through August 16th:
- Bayes, Jeffreys, prior distributions, and the philosophy of statistics – In this brief discussion I will argue the following: (1) in thinking about prior distributions, we should go beyond Jeffreys’s principles and move toward weakly informative priors; (2) it is natural for those of us who work in social and computational sciences to favor complex models, contra Jeffreys’s preference for simplicity; and (3) a key generalization of Jeffreys’s ideas is to explicitly include model checking in the process of data analysis.
- Does Confirmation Work the Same at Every Level of Science – In the 1960s, Kuhn maintained that there is no standard higher of rationality than the assent of the relevant community. Realists have seek to evaluate the rationality of science relative to a highest standard possible—namely the truth, or approximate truth, of our best theories. Given that the realist view of rationality is controversial, it seems that a more secure reply to Kuhn should be based on a less controversial objective of science—namely, the goal of predictive accuracy. Not only does this yield a more secure reply to Kuhn, but it also provides the foundation on which any realist arguments should be based. In order to make this case, it is necessary to introduce a three-way distinction between theories, models, and predictive hypotheses, and then ask some hard questions about how the methods of science can actually achieve their goals. As one example of the success of such a program, I explain how the truth of models can sometimes lower their predictive accuracy…
- What is a statistical model? – This paper addresses two closely related questions, “What is a statistical model?” and “What is a parameter?” The notions that a model must “make sense,” and that a parameter must “have a well-defined meaning” are deeply ingrained in applied statistical work, reasonably well understood at an instinctive level, but absent from most formal theories of modelling and inference. In this paper, these concepts are defined in algebraic terms, using morphisms, functors and natural transformations. It is argued that inference on the basis of a model is not possible unless the model admits a natural extension that includes the domain for which inference is required. For example, prediction requires that the domain include all future units, subjects or time points. Although it is usually not made explicit, every sensible statistical model admits such an extension. Examples are given to show why such an extension is necessary and why a formal theory is required…
- Are You Happy? – The New York Review of Books – Chances are if someone were to ask you, right now, if you were happy, you’d say you were. Claiming that you’re happy—that is, to an interviewer who is asking you to rate your “life satisfaction” on a scale from zero to ten—appears to be nearly universal, as long as you’re not living in a war zone, on the street, or in extreme emotional or physical pain. The Maasai of Kenya, soccer moms of Scarsdale, the Amish, the Inughuit of Greenland, European businessmen—all report that they are happy. When happiness researcher Ed Diener, the past president of the International Society of Quality of Life Studies, synthesized 916 surveys of over a million people in forty-five countries, he found that, on average, people placed themselves at seven on the zero-to-ten scale…
- Should Copyright Of Academic Works Be Abolished? – The conventional rationale for copyright of written works, that copyright is needed to foster their creation, is seemingly of limited applicability to the academic domain. For in
a world without copyright of academic writing, academics would still benefit from publishing in the major way that they do now, namely, from gaining scholarly esteem.
Yet publishers would presumably have to impose fees on authors, because publishers
would not be able to profit from reader charges. If these publication fees would be borne
by academics, their incentives to publish would be reduced. But if the publication fees
would usually be paid by universities or grantors, the motive of academics to publish
would be unlikely to decrease (and could actually increase) – suggesting that ending
academic copyright would be socially desirable in view of the broad benefits of a
copyright-free world…
These are my links for July 8th through July 29th:
- Should Copyright Of Academic Works Be Abolished? – The conventional rationale for copyright of written works, that copyright is needed to foster their creation, is seemingly of limited applicability to the academic domain. For in
a world without copyright of academic writing, academics would still benefit from publishing in the major way that they do now, namely, from gaining scholarly esteem.
Yet publishers would presumably have to impose fees on authors, because publishers
would not be able to profit from reader charges. If these publication fees would be borne
by academics, their incentives to publish would be reduced. But if the publication fees
would usually be paid by universities or grantors, the motive of academics to publish
would be unlikely to decrease (and could actually increase) – suggesting that ending
academic copyright would be socially desirable in view of the broad benefits of a
copyright-free world…
- BBC – Radio 4 In Our Time – Philosophy Archive – You can listen again to all the programmes online. The most recent programmes appear at the top of the page.
- [0907.1579] The Computational Power of Minkowski Spacetime – The Lorentzian length of a timelike curve connecting both endpoints of a classical computation is a function of the path taken through Minkowski spacetime. The associated runtime difference is due to time-dilation: the phenomenon whereby an observer finds that another's physically identical ideal clock has ticked at a different rate than their own clock. Using ideas appearing in the framework of computational complexity theory, time-dilation is quantified as an algorithmic resource by relating relativistic energy to an $n$th order polynomial time reduction at the completion of an observer's journey. These results enable a comparison between the optimal quadratic \emph{Grover speedup} from quantum computing and an $n=2$ speedup using classical computers and relativistic effects. The goal is not to propose a practical model of computation, but to probe the ultimate limits physics places on computation.
- How to choose a statistical test – This book has discussed many different statistical tests. To select the right test, ask yourself two questions: What kind of data have you collected? What is your goal? Then refer to Table 37.1.
- NPWRC :: Statistical Significance Testing – Four basic steps constitute statistical hypothesis testing. First, one develops a null hypothesis about some phenomenon or parameter. This null hypothesis is generally the opposite of the research hypothesis, which is what the investigator truly believes and wants to demonstrate. Research hypotheses may be generated either inductively, from a study of observations already made, or deductively, deriving from theory. Next, data are collected that bear on the issue, typically by an experiment or by sampling. (Null hypotheses often are developed after the data are in hand and have been rummaged through, but that's another topic.)
- Data Mining Techniques – Data Mining is an analytic process designed to explore data (usually large amounts of data – typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction – and predictive data mining is the most common type of data mining and one that has the most direct business applications.
These are my links for June 30th from 10:03 to 13:45:
- Statistical Resources on the Web/Environment –
- Data Mining Community’s Top Resource – Data Mining Community's Top Resource Since 1997 for Data Mining and Analytics Software, Jobs, Consulting, Courses, Education, News, and more.
- Time Series Analysis – In the following topics, we will first review techniques used to identify patterns in time series data (such as smoothing and curve fitting techniques and autocorrelations), then we will introduce a general class of models that can be used to represent time series data and generate predictions (autoregressive and moving average models). Finally, we will review some simple but commonly used modeling and forecasting techniques based on linear regression.
- Free Online Course Materials | MIT OpenCourseWare – Free lecture notes, exams, and videos from MIT.
No registration required.
- NIST/SEMATECH e-Handbook of Statistical Methods –
These are my links for June 21st through June 28th:
These are my links for June 19th through June 21st:
- Electronic Frontier Foundation | Defending Freedom in the Digital World – From the Internet to the iPod, technologies are transforming our society and empowering us as speakers, citizens, creators, and consumers. When our freedoms in the networked world come under attack, the Electronic Frontier Foundation (EFF) is the first line of defense. EFF broke new ground when it was founded in 1990 — well before the Internet was on most people's radar — and continues to confront cutting-edge issues defending free speech, privacy, innovation, and consumer rights today. From the beginning, EFF has championed the public interest in every critical battle affecting digital rights.
- Public Library of Science – The Public Library of Science (PLoS) is a nonprofit organization of scientists and physicians committed to making the world's scientific and medical literature a public resource.
- The R Project for Statistical Computing – R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
- Google Scholar – Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: peer-reviewed papers, theses, books, abstracts and articles, from academic publishers, professional societies, preprint repositories, universities and other scholarly organizations. Google Scholar helps you identify the most relevant research across the world of scholarly research.
- Google Trends – With Google Trends, you can compare the world’s interest in your favorite topics. Enter up to five topics and see how often they’ve been searched on Google over time. Google Trends also shows how frequently your topics have appeared in Google News stories, and in which geographic regions people have searched for them most.
- Google Insights for Search – With Google Insights for Search, you can compare search volume patterns across specific regions, categories, time frames and properties
- Google Squared – Google Squared takes a category and creates a starter 'square' of information, automatically fetching and organizing facts from across the web.
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