cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8079644.v2
title: Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

description: When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II–Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with <i>R Shiny</i> to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.
DOI: 10.6084/m9.figshare.8855948.v1
title: Hypothesis Testing in Presence of Adversaries

description: We present an extension to the classical problem of hypothesis testing by incorporating actions of an adversary who intends to mislead the decision-maker and attain a certain benefit. After presenting the general problem within an adversarial statistical decision theory framework, we consider the cases of adversaries who can either perturb the data received or modify the underlying data-generating process parametrically. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.13483345.v1
title: Assurance for sample size determination in reliability demonstration testing

description: Manufacturers are required to demonstrate that products meet reliability targets. A way to achieve this is with reliability demonstration tests (RDTs), where a number of products are put on test and the test is passed or failed according to a decision rule based on the observed outcomes. There are various methods for determining the sample size for RDTs, typically based on the power of a hypothesis test following the RDT or risk criteria. Bayesian risk criteria approaches combine the choice of sample size with the analysis of the test data while relying on the specification of acceptable and rejectable reliability levels. In this paper we offer an alternative approach to sample size determination based on the idea of assurance. This approach chooses the sample size to provide a specified probability that the RDT will result in a successful outcome. It separates the design and analysis of the RDT, allowing different priors for the producer and consumer. We develop the assurance approach for sample size calculations in RDTs for binomial and Weibull likelihoods and propose appropriate prior distributions for the design and analysis of the test. In each case, we illustrate the approach with an example based on real data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13341474.v1
title: Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model

description: Motivated by a multi-fidelity Weather Research and Forecasting (WRF) climate model application where the available simulations are not generated based on hierarchically nested experimental design, we develop a new co-kriging procedure called Augmented Bayesian Treed Co-Kriging. The proposed procedure extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent process in the multifidelity setting to account for non-stationary and potential discontinuities in the model outputs at different fidelity levels. Moreover, we introduce an efficient imputation mechanism which allows the practical implementation of co-kriging when the experimental design is non-hierarchically nested by enabling the specification of semi-conjugate priors. Our imputation strategy allows the design of an efficient RJ-MCMC implementation that involves collapsed blocks and direct simulation from conditional distributions. We develop the Monte Carlo recursive emulator which provides a Monte Carlo proxy for the full predictive distribution of the model output at each fidelity level, in a computationally feasible manner. The performance of our method is demonstrated on benchmark examples and used for the analysis of a large-scale climate modeling application which involves the WRF model. <i>Supplementary materials are available online.</i>
DOI: 10.6084/m9.figshare.13286479.v1
title: A Random Fourier Feature Method for Emulating Computer Models With Gradient Information

description: Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large datasets. We propose a random Fourier feature method to mitigate this difficulty. The key idea of the proposed method is to employ random Fourier features to obtain an easily computable, low-dimensional feature representation for shift-invariant kernels involving gradients. The effectiveness of the proposed method is illustrated by several examples.
DOI: 10.6084/m9.figshare.8016296
title: Process Monitoring ROC Curve for Evaluating Dynamic Screening Methods

description: In practice, we often need to sequentially monitor the performance of individual subjects or processes, so that interventions can be made in a timely manner to avoid unpleasant consequences (e.g., strokes or airplane crashes) once the longitudinal patterns of their performance variables deviate significantly from the regular patterns of well-functioning subjects or processes. Some statistical methods are available to handle this dynamic screening (DS) problem. Because the performance of the DS methods is related to their signal times, the conventional false positive rate (FPR) and false negative rate (FNR) cannot be effective in measuring their performance. So far, there is no existing metrics in the literature for properly measuring the performance of DS methods. In this article, we aim to fill this gap by proposing a new performance evaluation approach, called process monitoring receiver operating characteristic curve, which properly combines the signal times with (FPR,FNR). Numerical examples and theoretical justifications show that this approach provides an effective tool for measuring the performance of DS methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8209508
title: An Effective Method for Online Disease Risk Monitoring

description: Many diseases can be prevented or treated if they can be detected early or signaled before their occurrence. Disease early detection and prevention (DEDAP) is thus important for health improvement of our society. Traditionally, people are encouraged to check their health conditions regularly so that readings of relevant medical indices can be compared with certain threshold values and any irregular readings can trigger further medical tests to find root causes or diseases. One limitation of such traditional DEDAP methods is that they focus mainly on the data collected at the current time point and historical data are not fully used. Consequently, irregular longitudinal pattern of the medical indices could be neglected and certain diseases could be left undetected. In this article, we suggest a novel and effective new method for DEDAP. To detect a disease by this method, a patient’s risk to the disease is first quantified at each time point, and then the longitudinal pattern of the risk is monitored sequentially over time. A signal will be triggered by a large cumulative difference between the longitudinal risk pattern of the patient under monitoring and the longitudinal risk pattern of a typical person without the disease in concern. Both theoretical arguments and numerical studies show that it works well in practice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8344664
title: Student-<i>t</i> Processes for Degradation Analysis

description: Stochastic processes are widely used to analyze degradation data, and the Gaussian process is a particularly common one. In this article, we propose a robust statistical model using a Student-<i>t</i> process to assess the lifetime information of highly reliable products. This model is statistically plausible and demonstrates a substantially improved fit when applied to real data. A computationally accurate approach is proposed to calculate the first-passage-time density function of the Student-<i>t</i> degradation-based process; related properties are investigated as well. In addition, this article provides parameter estimation using the EM-type algorithm and a simple model-checking procedure to evaluate the appropriateness of the model assumptions. Several case studies are performed to demonstrate the flexibility and applicability of the proposed model with random effects and explanatory variables. Technical details, datasets, and R codes are available as supplementary materials.
DOI: 10.6084/m9.figshare.8016296.v2
title: Process Monitoring ROC Curve for Evaluating Dynamic Screening Methods

description: In practice, we often need to sequentially monitor the performance of individual subjects or processes, so that interventions can be made in a timely manner to avoid unpleasant consequences (e.g., strokes or airplane crashes) once the longitudinal patterns of their performance variables deviate significantly from the regular patterns of well-functioning subjects or processes. Some statistical methods are available to handle this dynamic screening (DS) problem. Because the performance of the DS methods is related to their signal times, the conventional false positive rate (FPR) and false negative rate (FNR) cannot be effective in measuring their performance. So far, there is no existing metrics in the literature for properly measuring the performance of DS methods. In this article, we aim to fill this gap by proposing a new performance evaluation approach, called process monitoring receiver operating characteristic curve, which properly combines the signal times with (FPR,FNR). Numerical examples and theoretical justifications show that this approach provides an effective tool for measuring the performance of DS methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8209508.v3
title: An Effective Method for Online Disease Risk Monitoring

description: Many diseases can be prevented or treated if they can be detected early or signaled before their occurrence. Disease early detection and prevention (DEDAP) is thus important for health improvement of our society. Traditionally, people are encouraged to check their health conditions regularly so that readings of relevant medical indices can be compared with certain threshold values and any irregular readings can trigger further medical tests to find root causes or diseases. One limitation of such traditional DEDAP methods is that they focus mainly on the data collected at the current time point and historical data are not fully used. Consequently, irregular longitudinal pattern of the medical indices could be neglected and certain diseases could be left undetected. In this article, we suggest a novel and effective new method for DEDAP. To detect a disease by this method, a patient’s risk to the disease is first quantified at each time point, and then the longitudinal pattern of the risk is monitored sequentially over time. A signal will be triggered by a large cumulative difference between the longitudinal risk pattern of the patient under monitoring and the longitudinal risk pattern of a typical person without the disease in concern. Both theoretical arguments and numerical studies show that it works well in practice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8209508.v2
title: An Effective Method for Online Disease Risk Monitoring

description: Many diseases can be prevented or treated if they can be detected early or signaled before their occurrence. Disease early detection and prevention (DEDAP) is thus important for health improvement of our society. Traditionally, people are encouraged to check their health conditions regularly so that readings of relevant medical indices can be compared with certain threshold values and any irregular readings can trigger further medical tests to find root causes or diseases. One limitation of such traditional DEDAP methods is that they focus mainly on the data collected at the current time point and historical data are not fully used. Consequently, irregular longitudinal pattern of the medical indices could be neglected and certain diseases could be left undetected. In this article, we suggest a novel and effective new method for DEDAP. To detect a disease by this method, a patient’s risk to the disease is first quantified at each time point, and then the longitudinal pattern of the risk is monitored sequentially over time. A signal will be triggered by a large cumulative difference between the longitudinal risk pattern of the patient under monitoring and the longitudinal risk pattern of a typical person without the disease in concern. Both theoretical arguments and numerical studies show that it works well in practice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8344664.v3
title: Student-<i>t</i> Processes for Degradation Analysis

description: Stochastic processes are widely used to analyze degradation data, and the Gaussian process is a particularly common one. In this article, we propose a robust statistical model using a Student-<i>t</i> process to assess the lifetime information of highly reliable products. This model is statistically plausible and demonstrates a substantially improved fit when applied to real data. A computationally accurate approach is proposed to calculate the first-passage-time density function of the Student-<i>t</i> degradation-based process; related properties are investigated as well. In addition, this article provides parameter estimation using the EM-type algorithm and a simple model-checking procedure to evaluate the appropriateness of the model assumptions. Several case studies are performed to demonstrate the flexibility and applicability of the proposed model with random effects and explanatory variables. Technical details, datasets, and R codes are available as supplementary materials.
DOI: 10.6084/m9.figshare.8344664.v2
title: Student-<i>t</i> Processes for Degradation Analysis

description: Stochastic processes are widely used to analyze degradation data, and the Gaussian process is a particularly common one. In this article, we propose a robust statistical model using a Student-<i>t</i> process to assess the lifetime information of highly reliable products. This model is statistically plausible and demonstrates a substantially improved fit when applied to real data. A computationally accurate approach is proposed to calculate the first-passage-time density function of the Student-<i>t</i> degradation-based process; related properties are investigated as well. In addition, this article provides parameter estimation using the EM-type algorithm and a simple model-checking procedure to evaluate the appropriateness of the model assumptions. Several case studies are performed to demonstrate the flexibility and applicability of the proposed model with random effects and explanatory variables. Technical details, datasets, and R codes are available as supplementary materials.
DOI: 10.6084/m9.figshare.8016296.v1
title: Process Monitoring ROC Curve for Evaluating Dynamic Screening Methods

description: In practice, we often need to sequentially monitor the performance of individual subjects or processes, so that interventions can be made in a timely manner to avoid unpleasant consequences (e.g., strokes or airplane crashes) once the longitudinal patterns of their performance variables deviate significantly from the regular patterns of well-functioning subjects or processes. Some statistical methods are available to handle this dynamic screening (DS) problem. Because the performance of the DS methods is related to their signal times, the conventional false positive rate (FPR) and false negative rate (FNR) cannot be effective in measuring their performance. So far, there is no existing metrics in the literature for properly measuring the performance of DS methods. In this article, we aim to fill this gap by proposing a new performance evaluation approach, called process monitoring receiver operating characteristic curve, which properly combines the signal times with (FPR,FNR). Numerical examples and theoretical justifications show that this approach provides an effective tool for measuring the performance of DS methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8297993
title: Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments

description: The mixture-of-mixtures (MoM) experiment is different from the classical mixture experiment in that the mixture component in MoM experiments, known as the major component, is made up of subcomponents, known as the minor components. In this article, we propose an additive heredity model (AHM) for analyzing MoM experiments. The proposed model considers an additive structure to inherently connect the major components with the minor components. To enable a meaningful interpretation for the estimated model, the hierarchical and heredity principles are applied by using the nonnegative garrote technique for model selection. The performance of the AHM was compared to several conventional methods in both unconstrained and constrained MoM experiments. The AHM was then successfully applied in two real-world problems studied previously in the literature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8326616
title: Bayesian State Space Modeling of Physical Processes in Industrial Hygiene

description: Exposure assessment models are deterministic models derived from physical–chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters such as generation and ventilation rates are usually of interest since they are difficult to obtain. In this article, we outline a flexible Bayesian framework for parameter inference and exposure prediction. In particular, we devise Bayesian state space models by discretizing the differential equation models and incorporating information from observed measurements and expert prior knowledge. At each time point, a new measurement is available that contains some noise, so using the physical model and the available measurements, we try to obtain a more accurate state estimate, which can be called filtering. We consider Monte Carlo sampling methods for parameter estimation and inference under nonlinear and non-Gaussian assumptions. The performance of the different methods is studied on computer-simulated and controlled laboratory-generated data. We consider some commonly used exposure models representing different physical hypotheses. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8297993.v3
title: Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments

description: The mixture-of-mixtures (MoM) experiment is different from the classical mixture experiment in that the mixture component in MoM experiments, known as the major component, is made up of subcomponents, known as the minor components. In this article, we propose an additive heredity model (AHM) for analyzing MoM experiments. The proposed model considers an additive structure to inherently connect the major components with the minor components. To enable a meaningful interpretation for the estimated model, the hierarchical and heredity principles are applied by using the nonnegative garrote technique for model selection. The performance of the AHM was compared to several conventional methods in both unconstrained and constrained MoM experiments. The AHM was then successfully applied in two real-world problems studied previously in the literature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8329967
title: A Bayesian Nonparametric Mixture Measurement Error Model With Application to Spatial Density Estimation Using Mobile Positioning Data With Multi-Accuracy and Multi-Coverage

description: The development of mobile network technologies has made it possible to collect location data of mobile devices through various positioning technologies. The location data can be used to estimate the spatial density of mobile devices, which in turn can be used by mobile service providers to plan for network capacity improvements. The two most prevalent positioning technologies are the assisted global positioning system (AGPS) and cell tower triangulation (CTT) methods. AGPS data provide more accurate location information than CTT data but can cover only a fraction of mobile devices, while CTT data can cover all mobile devices. Motivated by this problem, we propose a Bayesian nonparametric mixture measurement error model to estimate the spatial density function by integrating both noise-free data (i.e., AGPS data) and data contaminated with measurement errors (i.e., CTT data). The proposed model estimates the true latent locations from contaminated data, and the estimated latent locations, combined with noise-free data, are used to infer the model parameters. We model the true density function using a Dirichlet process (DP) mixture model with a bivariate beta distribution for the mixture kernel and a DP prior for the mixing distribution. The use of bivariate beta distributions for the mixture kernel allows the density function to have various shapes with a bounded support. Moreover, the use of a DP prior for the mixing distribution allows the number of mixture components to be determined automatically without being specified in advance. Therefore, the proposed model is very flexible. We demonstrate the effective performance of the proposed model via simulated and real-data examples.
DOI: 10.6084/m9.figshare.8297993.v2
title: Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments

description: The mixture-of-mixtures (MoM) experiment is different from the classical mixture experiment in that the mixture component in MoM experiments, known as the major component, is made up of subcomponents, known as the minor components. In this article, we propose an additive heredity model (AHM) for analyzing MoM experiments. The proposed model considers an additive structure to inherently connect the major components with the minor components. To enable a meaningful interpretation for the estimated model, the hierarchical and heredity principles are applied by using the nonnegative garrote technique for model selection. The performance of the AHM was compared to several conventional methods in both unconstrained and constrained MoM experiments. The AHM was then successfully applied in two real-world problems studied previously in the literature. Supplementary materials for this article are available online.