Model fitting and evaluation in climate reconstruction of tree-ring data: A comment on Steinschneider et al. (2017): Hierarchical regression models for dendroclimatic standardization and climate reconstruction

Steinschneider et al. (2017) investigate model choices made in the hierarchical climate reconstruction approach of Schofield et al. (2016). We identify two flaws in their approach. The first is the use of an unusual approximation to Bayesian inference that unnecessarily discards important information. The second is that they mischaracterize the robustness of their reconstructions due to overlooking important features of the out-of-sample predictions. We demonstrate how full Bayesian inference can be conducted with no additional effort, providing R/JAGS code. We also show how graphical visualization of the out-of-sample predictions can lead to better understanding and comparison of the models fitted.     

数值诊断的一种新方法──贝叶斯定理在数值诊断中的应用研究

根据贝叶斯定理,给出数值诊断的一种新方法——评分法．该方法简单易行,便于推广,且在治疗腰椎间盘突出症应用中收到满意效果．     

Cortinarius sect. Brunnei (Basidiomycota, Agaricales) in North Europe

The section Brunnei was extensively studied based on material from North Europe. To stabilise the nomenclature we studied the relevant types of taxa included in this section. Phylogenetic relationships and species limits were investigated using rDNA ITS sequences and the results were compared with the morphological data. We recognised 11 species: Cortinarius brunneus, C. clarobrunneus comb. nov., C. coleoptera, C. ectypus, C. gentilis, C. glandicolor (neotypified), C. pseudorubricosus, and four species described as new C. caesiobrunneus, C. albogaudis, C. carabus, and C. cicindela. They are described here and their taxonomy, ecology, distribution, and relationships are discussed. In addition, a key to species of the section Brunnei is provided. A total of 77 new sequences of 11 species are published including nine type sequences. Also the taxonomic assignments of sequences in the public databases belonging to the section Brunnei are revised.     

Accounting for multiplicity in the evaluation of "signals" obtained by data mining from spontaneous report adverse event databases

Surveillance of drug products in the marketplace continues after approval, to identify rare potential toxicities that are unlikely to have been observed in the clinical trials carried out before approval. This surveillance accumulates large numbers of spontaneous reports of adverse events along with other information in spontaneous report databases. Recently developed empirical Bayes and Bayes methods provide a way to summarize the data in these databases, including a quantitative measure of the strength of the reporting association between the drugs and the events. Determining which of the particular drug-event associations, of which there may be many tens of thousands, are real reporting associations and which random noise presents a substantial problem of multiplicity because the resources available for medical and epidemiologic followup are limited. The issues are similar to those encountered with the evaluation of microarrays, but there are important differences. This report compares the application of a standard empirical Bayes approach with micorarray-inspired methods for controlling the False Discovery Rate, and a new Bayesian method for the resolution of the multiplicity problem to a relatively small database containing about 48,000 reports. The Bayesian approach appears to have attractive diagnostic properties in addition to being easy to interpret and implement computationally.     

恶性卵巢肿瘤的Bayes预测

文献〔1〕通过逐步回归确定了与卵巢肿瘤有关的五个主要因素:年龄(X_1)、生育关系(x_2)、肿瘤大小(x_3)、肿瘤硬度(X_4)、并发症(X_5),并将这五个主要因素的指标分级如表1,给出最大似然诊断法。本文在此基础上,根据延边医学院1981年1月至1985年12月底经手术治疗的237例肿瘤病人的统计数据,对〔1〕的结果进一步研究与改进,给出了恶性卵巢肿瘤的Bayes诊     

Extrapolation in Risk Assessment: Improving the Quantification of Uncertainty,and Improving Information to Reduce the Uncertainty

Risk assessments inevitably extrapolate from the known to the unknown. The resulting calculation of risk involves two fundamental kinds of uncertainty: uncertainty owing to intrinsically unpredictable (random) components of the future events, and uncertainty owing to imperfect prediction formulas (parameter uncertainty and error in model structure) that are used to predict the component that we think is predictable. Both types of uncertainty weigh heavily both in health and ecological risk assessments. Our first responsibility in conducting risk assessments is to ensure that the reported risks correctly reflect our actual level of uncertainty (of both types). The statistical methods that lend themselves to correct quantification of the uncertainty are also effective for combining different sources of information. One way to reduce uncertainty is to use all the available data. To further sharpen future risk assessments, it is useful to partition the uncertainty between the random component and the component due to parameter uncertainty, so that we can quantify the expected reduction in uncertainty that can be achieved by investing in a given amount of future data. An example is developed to illustrate the potential for use of comparative data, from toxicity testing on other species or other chemicals, to improve the estimates of low-effect concentration in a particular case with sparse case-specific data.     

The role of game management in wildlife populations: uncertainty analysis of expert knowledge

Uncertainties about future states of wildlife populations make it difficult to pre-adapt to possible threats and ensure sustainability of resources and harvesting over the long term. This uncertainty is partly due to the unknown impact and future states of many factors that explain population sizes and variation. In this paper, the effect of local game management activities on the uncertainty of future population sizes of groups of Finnish wildlife species (ungulates, forest grouse, large predators, small predators and mountain hare) was analysed using expert knowledge and the Bayesian belief networks (BBNs) modelling techniques. As a result, the current knowledge and agreement of the relationships between wildlife population sizes and the game management activities explaining their variation as well as trends are evaluated. Information given to hunters and the number of hunters were seen as the most effective factors for the management of game populations. However, there were great uncertainties in the expectations regarding future trends in the management activities, especially in feeding, and there was disagreement in the direction of the trend in the length of the hunting season. The trends in the size of forest grouse populations were viewed as the most uncertain trend among species groups. At the same time, forest grouse were seen as the most regulated species group by local game management. Among interest variables, experts were very uncertain and they disagreed about the direction of the trend in the recreational value of hunting.     

Bayesian Estimation Using Ranked Set Sampling

Bayesian Estimation of the parameter of a distribution is considered using Ranked set sampling (RSS). It is shown that for at least one RSS plan, the Bayes estimator has smaller Bayes risk than the Bayes estimator using simple random sampling (SRS). Furthermore, for exponential family with conjugate prior, the Bayes estimator of the mean using balanced RSS dominates, in terms of its Bayes risk, the Bayes estimator of the mean using SRS. This procedure is used to estimate the average Milk yield of four hundreds and two sheep. The empirical efficiency supports the theoretical findings.     

Topology-Bayes versus Clade-Bayes in phylogenetic analysis

Several features of currently used Bayesian methods in phylogenetic analysis are discussed. The distinction between Clade-Bayes and Topology-Bayes is presented and illustrated with an empirical example. Three problems with Bayesian phylogenetic methods--exaggerated clade support, inconsistently biased priors, and the impossibility of hypothesis testing of cladograms--are shown to be the result of using a Clade-based Bayesian approach. Topology-based Bayesian methods do not share these shortcomings.     

Estimation of Exponential Parameters in the Presence of Outliers

In the present paper we discuss the estimation of the parameter and survival function of an exponential population. It is assumed that the sample based on which the estimation is done, contains k outliers that are also exponentially distributed with parameter, a constant multiple of that of the original distribution.