A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d. samples drawn from a distribution q that is distinct from p. The distributions p and q are assumed to be unknown in advance. Distribution-free tests are constructed using maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size m, the number s of anomalous sequences and the number n of sequences. It is then shown that with s known, the constructed test is exponentially consistent if m is greater than a constant factor of log n, for any p and q, whereas with s unknown, m should has an order strictly greater than log n. Furthermore, it is shown that no test can be consistent for arbitrary p and q if m is less than a constant factor of log n, thus the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that our tests outperform (or perform as well as) the tests based on other competitive approaches under various cases.

As we are all fond of saying, innovation follows pain points. Are we missing something in our uber-critical search capabilities that needs to be resolved? A colleague recently pointed me to a slim volume "Structured Search for Big Data" by Mikhail Gilula (published by Elsevier and available on Amazon) that argues that not only are our search tools deficient but that a complete revamp of the underlying key-word NoSQL DB structure is what's required. Use Google, Amazon, or any of the other life-critical search tools we've become so reliant upon and you are using key-word search on NoSQL. The pain that Gilula identifies is the length of time it takes the consumer to research and select complex merchandise for best deals resulting from the imprecision of the search results.

The goal of genome-wide association studies (GWAS) (e.g. the WTCCC study1) is to examine the relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and individual traits, which are usually complex diseases or behavioral characteristics. Generally, a large number of statistical tests are performed in parallel, each SNP being individually tested for association2,3,4. The standard approach consists of computing individual, SNP-specific p-values corresponding to a statistical association test and comparing these p-values against some given significance threshold (say t*), meaning that precisely those SNPs with p-values smaller than t*are declared to be associated with the trait4,5,6. We refer to this approach as raw p-value thresholding (RPVT) and review some standard methods for choosing t*for the purpose of controlling multiple type I error rates (in particular, the family-wise error rate (FWER) and the expected number of false rejections (ENFR)) in the Methods Section. According to the GWAS catalog7,8 (last accessed 03-07-2015), the more than 1,400 GWAS published so far have led to the identification of more than 11,000 SNPs associated with about 800 human diseases and anthropometric traits with p-values using t* 1 10 5.

As we are all fond of saying, innovation follows pain points. Are we missing something in our uber-critical search capabilities that needs to be resolved? A colleague recently pointed me to a slim volume "Structured Search for Big Data" by Mikhail Gilula (published by Elsevier and available on Amazon) that argues that not only are our search tools deficient but that a complete revamp of the underlying key-word NoSQL DB structure is what's required. Use Google, Amazon, or any of the other life-critical search tools we've become so reliant upon and you are using key-word search on NoSQL. The pain that Gilula identifies is the length of time it takes the consumer to research and select complex merchandise for best deals resulting from the imprecision of the search results.

STOCKHOLM – It is serendipity: from America to Viagra, history is full of great discoveries helped along by chance, as more than a century of Nobel prizes can attest. Among the chance discoveries that have been honored with the prestigious prize are X-rays (physics, 1901), penicillin (medicine, 1945), fullerenes that paved the way for nanotechnology (chemistry, 1996), conductive polymers (chemistry, 2000), and the bacteria responsible for ulcers (medicine, 2005). But, as the father of pasteurization Louis Pasteur noted in 1854, "In the fields of observation, chance only favors the prepared mind" -- a remark made in reference to the discovery of the link between electricity and magnetism by Danish scientist Hans Christian Orsted. Orsted happened to notice that a compass needle deflected from magnetic north when an electric current from a battery was switched on and off -- a pioneering discovery in electromagnetism. Like Pasteur, Dutch scientist Pek Van Andel also believes in the unexpected.

This is the conference, and here's my talk (will do Google hangout, just as with my recent talks in Bern, Strasbourg, etc): Through a series of examples, we consider problems with classical hypothesis testing, whether performed using classical p-values or confidence intervals, Bayes factors, or Bayesian inference using noninformative priors. We locate the problem not in the use of any particular statistical method but rather with larger problems of deterministic thinking and a misguided version of Popperianism in which the rejection of a straw-man null hypothesis is taken as confirmation of a preferred alternative. We suggest solutions involving multilevel modeling and informative Bayesian inference. The post Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2:30pm Thurs 15 Sept) appeared first on Statistical Modeling, Causal Inference, and Social Science. The post Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2:30pm Thurs 15 Sept) appeared first on All About Statistics.

Philosophers of science are not known for agreeing with each other--contrariness is part of the job description. But for thousands of years, from Aristotle to Thomas Kuhn, those who study what science is have roughly categorized themselves into two basic camps: "realists" and "anti-realists." In philosophical terms, "anti-realists" or "empiricists" understand science as investigating the properties of observable objects via experiments. Empirical theories are constrained by the experimental results. "Realists," on the other hand, speculate more freely about the possible shape of the unobservable world, often designing mathematical explanations that cannot (yet) be tested. Isaac Newton was a realist, as are string theorists. Most scientists do not lose sleep worrying about philosophical divides. But maybe they should; Albert Einstein certainly did, as did Niels Bohr, and Erwin Schrödinger.

There's one recurring obsession that keeps haunting almost every marketing and ad agency executive. It's in nearly every Powerpoint deck and on every marketer's mind… Millennials: the oh so documented and ubiquitous M Word!. For many years, most marketers (and their agencies) relied on the same old recipe. Brand awareness and reach were the only metrics that mattered. Problem is, the recipe started to turn sour.

Lauded by hippies, music heads and fans of all things psychedelic, acid has been used to blur the boundaries of reality and perception for decades. Experts have drawn parallels between the dissociative effects caused by the drug on the brain and psychiatric illnesses, with hope it could potentially be explored as a treatment. Now researchers have shown how the drug affects language and speech, reveal it may even enable users to be more creative. In the trials, participants took between 40 to 80 micrograms of the drug intravenously, which would be in the same range as the average tab of acid (illustrated). Researchers in Germany and the UK carried out trials in which participants were asked to name a number of pictures, either under the influence of acid or taking a placebo.

From the era of the desktop app to the era of the web page to the era of the mobile app to the latest paradigm shift which seems to be happening now: the conversation. These providers will most likely sit at the center of an ecosystem which will handle NLP (Natural Language Processing), semantic analysis, and other core tasks such as location and calendar integration. Currently, there are "bits and pieces" for particulars like dialogs (IBM Dialog) and NLP (IBM AlchemyAPI) all the way to large sdk's for voice and digital assistants (Alexa, Siri, and Google). While the examples above are simplistic they do provide some structure and a view into the basic text lines of voice and chat applications.