Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.     

Binary regression with misclassified response and covariate subject to measurement error: a bayesian approach

We consider a Bayesian analysis for modeling a binary response that is subject to misclassification. Additionally, an explanatory variable is assumed to be unobservable, but measurements are available on its surrogate. A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response. Unlike existing methods, no model parameters need be assumed known. Markov chain Monte Carlo methods are utilized to perform the necessary computations. The methods developed are illustrated using atomic bomb survival data. A simulation experiment explores advantages of the approach.     

蛋白质二级结构预测的系统误差

目前蛋白质二级结构的预测准确率徘徊在75%左右,难以作进一步提高。本文通过统计学的方法,对蛋白质的冗余数据库进行了分析。并由此证明,目前影响预测准确率继续的真正原因是蛋白质数据库本身的系统误差,系统误差大约为25%。而该误差是由于实验条件的客观原因带来的。     

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.     

一个血吸虫病数学模型的交替方向有限元分析

摘要：对血吸虫病数学模型利用交替方向Galerkin方法逼近模型(P)的解,并利用微分方程先验估计理论和技巧,进行近似解的收敛性分析,得到其最优L^2模误差估计．     

生物多样性价值的经济学处理：一些理论障碍及其克服

本文首先对国际生态经济学界围绕“世界生态系统功能价值计量”的方法学论争进行短简的评价,接着阐述了生物多样性价值的新的概念框架,并据此就生物多样性价值计量中尚未得到关注的问题（生物多样性价值的可计算性,价值计量方法的恰当性,价值分量的可加性和可解析性,价值误差测算）提出解决思路,本文最后讨论了两个保护经济学问题方法学构架,其一是生物多样性破坏的经济损失计算,其二是生物多样性保护工程的成本－效益体质与成本－效果分析。     

Scale‐dependent variation in visual estimates of grassland plant cover

Abstract. Plant cover was visually estimated by five observers, independent of each other, in a species‐rich grassland in the Bílé Karpaty Mts., southeastern Czech Republic, in seven plots ranging from 0.001 to 4 m². Variation of total plant cover among the observers was high at small scales: 0.001–0.016 m²; coefficient of variation, CV = 35 to 45%, but much lower at larger scales: 0.06–4 m²; CV = 7 to 15%. Differences between visual estimates of plant cover of individual species made by different observers were affected by plot size, total cover and morphology of particular plants. CV of the cover of individual species ranged from 0 to 225% and decreased with increasing plot size. For abundant plants the CV attained ca. 50%, independent of plot size. In spite of a very high number of sterile plants with similar leaf morphology and colour, the observed variation in cover estimates in the studied grassland was comparable with results reported from other vegetation types. Differences between estimates by individual observers were often larger than usual year to year changes in undisturbed grasslands. Therefore, I suggest that to avoid difficulties in the interpretation of results based on plant cover data obtained from visual estimates, several observers should always work together, adjusting their extreme estimates.     

Parametric and Parametrically Smoothed Distribution-Free Proportional Hazard Models with Discrete Data

This paper discusses discrete time proportional hazard models and suggests a new class of flexible hazard functions. Explicitly modeling the discreteness of data is important since standard continuous models are biased; allowing for flexibility in the hazard estimation is desirable since strong parametric restrictions are likely to be similarly misleading. Simulation compare continuous and discrete models when data are generated by grouping and demonstrate that simple approximations recover underlying hazards well and outperform nonparametric maximum likelihood estimates in term of mean squared error.