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The bulk elemental composition of soil subsurface (B) horizons is influenced by environmental, biological, geological, and climatic factors. Because fossil soils (paleosols) are common in the geologic record, quantitative models that link climate to paleosol geochemistry are highly desirable in the paleoclimate community. Error associated with these models is typically reported as the root mean square error (RMSE) of a regression analysis and reflects the variability imparted by non-climatic influences on soil formation and the uncertainty associated with model fitting. However, for prediction purposes, the RMSE is well known to underestimate model uncertainty. In this work we re-evaluate a widely used transfer function for mean annual precipitation (MAP) based on the chemical index of alteration minus potassium (CIA-K) using data science best practices on two continental-scale soil data sets. Data set inter-comparisons and cross-validation of exponential regression models indicate that the root mean square prediction error (RMSPE) between CIA-K and MAP for soils representative of climates across the continental United States is around 299 mm, significantly higher than the currently accepted 182 mm RMSE. Further, CIA-K is unable to predict perhumid (>2000 mm MAP) climate zones. We show that transitioning from a simple regression framework to one of recursive partitioning via random forests can significantly increase prediction accuracy while automating variable selection. We introduce two new, widely applicable random forest models for MAP (RF-MAP) that use 10 elemental oxides as input variables and were calibrated on the Baylor University Soil Informatics (BU-SI) data set. RF-MAP version 1.0 (RF-MAP1.0) was generated using the entire BU-SI data set (n = 685) and can predict MAP values up to 6865 mm with a RMSPE of 395 mm. RF-MAP version 2.0 (RF-MAP2.0) was generated using a modification of the BU-SI data set (n = 642) and can predict MAP values up to ∼1600 mm with a RMSPE of 209 mm. Pruned regression trees provide insight into the mechanisms driving the random forest models and demonstrate the first empirical confirmation of the sensitivity of soil elemental responses to global climate zones. The RF-MAP1.0 and RF-MAP2.0 models predict MAP values comparable to independent proxy estimates for a range of deep-time paleosols. We advocate for application of RF-MAP1.0 in settings where no a priori information on paleoclimate is available, and encourage the use of either RF-MAP1.0 or RF-MAP2.0 if users have independent constraints that paleo-MAP was below 1600 mm.

Standard prior elicitation procedures require experts to explicitly quantify their beliefs about parameters in the form of multiple summaries. In this article, we draw on recent advances in the statistical graphics and information visualization communities to propose a novel elicitation scheme that implicitly learns an expert’s opinions through their sequential selection of graphics of carefully constructed hypothetical future samples. While the scheme can be applied to a broad array of models, we use it to construct procedures for elicitation in data models commonly used in practice: Bernoulli, Poisson, and Normal. We also provide open-source, web-based Shiny implementations of the procedures.

This article provides necessary and sufficient conditions that characterize independence among sub-vectors of a singular multivariate skew-normal random vector.

Wald, profile likelihood, and marginal likelihood confidence intervals are derived for the ratio of two Poisson rates in the presence of one-way differentially misclassified data using double sampling. Monte Carlo simulations demonstrate the reliability and relative performance of the intervals, and an example from cancer epidemiology illustrates their application and interpretation in a real-world scenario. All of the methods described are implemented and freely available in the R package poisDoubleSamp on the Comprehensive R Archive Network (CRAN).

In this study we present evidence supporting the view that people’s perceived risk of hurricane-related hazards can be reduced to a single seriousness score that spans different hurricane-induced risk types and that compliant behavior with official advisories is strongly dependent on whether one perceives a high risk with respect to any type of hurricane-related hazards. Our analysis suggests that people are less sensitive to risk type than they are to the general seriousness of the risks. Using this single seriousness score, representing a composite risk measure, emergency managers can be informed about the severity of the public’s risk perceptions to impending hurricane hazards and might better craft their public directives in ways that minimize disruptive evacuations and achieve greater compliance with government directives.

One of the key advantages of the Bayesian paradigm is the ability to incorporate past experiences and expert opinion into statistical analyses. However, the principled, precise distillation of expert opinion into a probability distribution, a task known as prior elicitation, is challenging and involves considerations from psychology, computational science, software engineering, and other related fields. Moreover, for elicitation to be practical, applied statisticians need good computer tools to enable its use–to perform complex calculations and provide feedback. Such computer programs are called facilitators. Using the prior elicitation of a population proportion as a canonical example, in this article we contend that Wolfram Research Inc.’s Mathematica/CDF Player/CDF suite of technologies provides a new state-of-the-art platform for the development and dissemination of free facilitators. To illustrate its capabilities, we present a free facilitator that enables, in a real-time interactive environment, the computations and diagnostics required of an elicitation method known as the mode/percentile (MP) method.

Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis. We extend previous work on Poisson regression models that allow for misclassification by thoroughly discussing the basis for the models and allowing for extra-Poisson variability in the form of random effects. Via simulation we show the improvements in inference that are brought about by accounting for both the misclassification and the overdispersion.

Sample size determination is a vital part of clinical studies where cost and safety concerns lead to greater importance of not using more subjects and resources than are required. The Bayesian approach to sample size determination has the advantages of being able to use prior data and expert opinion to possibly reduce the total sample size while also acknowledging all uncertainty at the design stage. We apply a Bayesian decision theoretic approach to the problem of comparing two Poisson rates and find the required sample size to obtain a desired power while controlling the Type I error rate.

Aim One of Mayo Clinic’s missions is to standardize care through an integrated clinical practice. Reporting histocompatibility results is very complex and HLA labs have different ways of interpreting results. By standardizing across the three sites we improve the interchangeability of test results, reduce resources to support our practice, provide the best standard of care for our patients and support for our physicians for the Mayo paired kidney exchange program. Methods Integration was defined as: Each lab will use HistoTrac as the lab management system. HistoTrac will interface with the lab integrated system (LIS) for processing of orders and reporting results to the medical record. Each lab will report the same discreet result values and interpretive reports. Each lab will use the same vendor and lot number if possible for antibody and typing tests. A project group was created which includes: a director and technical specialist from each site, a business analyst, and a computer programmer. This team worked together using conference calls and web meetings to establish standardization for reporting HLA typing, antibody and crossmatch results. Results The results reported at each site will use the same test definitions, reporting templates, and will be transferred to the LIS. We encountered some obstacles due to: lab practice differences, transplant policies and business requirements. The convergence effort is still ongoing at each site: MN: update reporting and implement interpretive reports AZ: implement an interface to the LIS and update interpretive reports FL: implement HistoTrac with an interface to the LIS and update interpretive reports Conclusion All histocompatibility data and interpretive reports will be available in the medical record at each mayo site. Convergence of reporting test results will improve the communication and understanding when evaluating a patient while making the process cost effective. The next step in convergence is reporting to one medical record at all three sites. Download full-size image

The mpoly package is a general purpose collection of tools for symbolic computing with multivariate polynomials in R. In addition to basic arithmetic, mpoly can take derivatives of polyno- mials, compute Grobner bases of collections of polynomials, and convert polynomials into a functional form to be evaluated. Among other things, it is hoped that mpoly will provide an R-based foundation for the computational needs of algebraic statisticians.

In this article, I introduce a novel applet (“module”) for exploring probability distributions, their samples, and various related statistical concepts. The module is primarily designed to be used by the instructor in the introductory course, but it can be used far beyond it as well. It is a free, cross-platform, stand-alone interactive application based on Wolfram Research's novel computable document format (CDF) technology. It features over thirty common discrete and continuous distributions and can be used to illustrate concepts such as random samples, population and sample means and medians, histograms, kernel density estimators, boxplots, and cumulative distribution, survival, and hazard functions all while dynamically linking samples and estimators to adjustable distribution parameters in real-time. Additionally, the module includes real-world datasets to aid in communicating the concept of fitting a distribution to data. It is hoped that the module will be helpful to instructors at both the high sc...