Massive Sample Size Research Articles

고차원 대용량 자료분석의 현재 동향

빅 데이터의 출현은 여러가지 과학적 난제에 대답 할 수 있는 기회를 제공하지만 흥미로운 도전을 또한 제공한다. 이러한 빅데이터의 주요 특징으로 "고차원"과 "대용량"을 들 수가 있다. 본 논문은 이러한 두 가지 특징에 동반되는 다음과 같은 도전문제에 대한 개요를 제시한다 : (1) 고차원 자료에서의 소음 축적과 위 상관 관계; (ii) 대용량 자료분석을 위한 계산 확장성. 또한 본 논문에서는 재난예측, 디지털 인문학과 세이버메트릭스 등 다양한 분야에서 빅 데이터의 다양한 응용사례를 제공한다. The advent of big data brings the opportunity to answer many open scientic questions but also presents some interesting challenges. Main features of contemporary datasets are the high dimensionality and massive sample size. In this paper, we give an overview of major challenges caused by these two features: (1) noise accumulation and spurious correlations in high dimensional data; (ii) computational scalability for massive data. We also provide applications of big data in various fields including forecast of disasters, digital humanities and sabermetrics.

Stock Returns and Investorss Mood: Good Day Sunshine or Spurious Correlation?

This paper critically evaluates the statistical significance of the weather effect on stock return reported in two seminal studies. It is found that their research design is statistically flawed and severely biased against the null hypothesis of no effect. This, coupled with the test statistics inflated by massive sample sizes, strongly suggests that the statistical significance is spurious as an outcome of Type I error. The alternatives to the p-value criterion for statistical significance soundly support the null hypothesis of no weather effect. As an application, the effect of daily sunspot numbers on stock return is examined. Under the same research design as that of a seminal study, the number of sunspots is found to be highly statistically significant although its economic impact on stock return is negligible.

Can Genetic Analysis of Putative Blood Alzheimer's Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10-7. Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.

Constrained Semi-Supervised Growing Self-Organizing Map

Semi-supervised clustering tries to surpass the limits of unsupervised clustering using extra information contained in occasional labeled data points. However, providing such labeled samples is not always possible or easy in real world applications. A weaker, yet still very useful option is providing constraints on the unlabeled training samples, which is the focus of the Constrained Semi-Supervised (CSS) clustering. On the other hand, online learning has gained considerable amount of interests in real world problems with massive sample size or streaming behavior, as lack of memory and computational resources seriously restrict the application of the offline and batch methods. However, the existing algorithms for online CSS clustering problem either assumed that the entire dataset is available and added constraints incrementally or considered chunks of constrained data points and applied an offline CSS clustering algorithm. Thus, none of them can be categorized as a genuine online CSS clustering algorithm. In this paper, we propose CS2GS, an online CSS clustering algorithm. CS2GS is constructed by modifying the online learning process of Semi-Supervised Growing Self-Organizing Map, and converting it to an iterative constrained metric learning problem that can be solved using the Bregman׳s iterative projections. The proposed CS2GS is studied via a series of thorough tests using synthetic and real data including selections from UCI datasets and FEP – a recent bilingual corpus used for sentence aligning stage of machine translation. Experimental results show the effectiveness of CS2GS in online CSS clustering, and prove that indeed, the limits of the system accuracy may be pushed higher using unlabeled samples.

Challenges of Big Data Analysis.

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

Rumors of the death of consumer genomics are greatly exaggerated

Rumors of the death of consumer genomics are greatly exaggerated

The Psychiatric GWAS Consortium: Big Science Comes to Psychiatry

The Psychiatric GWAS Consortium was founded with the aim of conducting statistically rigorous and comprehensive GWAS meta-analyses for five major psychiatric disorders: ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia. In the era of GWAS and high-throughput genomics, a major trend has been the emergence of collaborative, consortia approaches. Taking advantage of the scale that collaborative consortia approaches can bring to a problem, the PGC has been a major driver in psychiatric genetics and provides a model for how similar approaches may be applied to other disease communities.

The brain as a central key organ.

Sometimes progress is hard to see, when looking at the big picture, because there is very little of it. But sometimes progress is hard to see because the big picture is out of focus. When perioperative deaths ascribed to anaesthesia are in the order of 1 in 20,000 operations and even changes in major morbidity require massive sample sizes to detect, neuroanaesthesia's most emphatic yardstick of progress is too crude to measure advances that have occurred over the most recent decade. We clearly need to become more familiar with neuropsychological tests that can detect subtle changes. Today, for elective neurosurgery, we are primarily in the business of doing two things--pushing the envelope for surgical intervention to include cases that would have been considered too risky 15 years ago, and reducing the frequency of "Uncle-Joe-has-never-been-the-same-since-they-operated-on-his-brain syndrome". Both of these areas of progress are empirically measurable, but we have not made much progress towards measuring them. Of course, this measurement problem plagues anaesthesiology generally, and we need to attend to it in general. Meanwhile, saying where we are relative to the recent past and the near future involves a lot of guesswork. What follows is my guess-work about progress in neurosurgical anaesthesiology.