Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

舟山灯塔历史概述

灯塔一般位于海岸、港口或河道，用以指引船只的航行方向。灯塔大都类似塔的形状，透过塔顶的透镜系统，将光芒射向海面或河面照明。舟山地处我国大陆海岸线中部，长江口南侧，杭州湾外缘东海洋面上，自古有“江浙之门户，四明之藩篱”之称，历史上就为中国重要的海上贸易通道。为保障海上航行安全，舟山很早就设立导航设施，到清晚期，随着航线的日益繁忙以及导航技术的发展，舟山设立了众多的近代灯塔，这些灯塔大多保留完整，其数量之多，密度之高，在全国首屈一指。     

Inferences on the Means of Two Log-Normal Distributions: A Computational Approach Test

In this article, we propose a novel approach for testing the equality of two log-normal populations using a computational approach test (CAT) that does not require explicit knowledge of the sampling distribution of the test statistic. Simulation studies demonstrate that the proposed approach can perform hypothesis testing with satisfying actual size even at small sample sizes. Overall, it is superior to other existing methods. Also, a CAT is proposed for testing about reliability of two log-normal populations when the means are the same. Simulations show that the actual size of this new approach is close to nominal level and better than the score test. At the end, the proposed methods are illustrated using two examples.     

Testing equality of two normal covariance matrices with monotone missing data

Abstract

The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.     

疲劳裂纹扩展试验裂纹图像采集系统

针对目前金属疲劳裂纹扩展检测方法存在效率低、操作繁琐、精度不高，不能实现实时性等缺点，文章提出了基 于机器视觉的裂纹检测方法。根据金属疲劳裂纹扩展试验过程中裂纹图像的采集要求，设计了摄像头位置可自动调整 的疲劳裂纹图像采集系统，包括摄像头安装运动装置，裂纹图像采集装置以及摄像头运动控制系统。图像采集装置由高 分辨率黑白面阵CCD摄像头、光学镜头，光源照明装置和图像采集卡组成，前3者安装在摄像头支架上，与摄像头运动 装置相连。摄像头运动装置中的X，Y，，Z轴运动机构由步进电机带动滚珠导轨机构。在基于ARM技术的运动控制系统 的控制下摄像头可沿X，Y，，Z轴作高精度直线运动，实现和试件之间精确位置的调整。实验结果表明，所设计的系统可 采集到疲劳裂纹扩展试验过程中高清晰度、高分辨率的试件裂纹图像，满足下一步裂绞尺寸计算的要求；摄像头运动定 位精度可达到0. 02 mm，满足精确的系统聚焦、位置调整、跟踪采集的要求。     

Statistical testing strategies for assessing treatment efficacy and marker accuracy in phase III trials

When a candidate predictive marker is available, but evidence on its predictive ability is not sufficiently reliable, all‐comers trials with marker stratification are frequently conducted. We propose a framework for planning and evaluating prospective testing strategies in confirmatory, phase III marker‐stratified clinical trials based on a natural assumption on heterogeneity of treatment effects across marker‐defined subpopulations, where weak rather than strong control is permitted for multiple population tests. For phase III marker‐stratified trials, it is expected that treatment efficacy is established in a particular patient population, possibly in a marker‐defined subpopulation, and that the marker accuracy is assessed when the marker is used to restrict the indication or labelling of the treatment to a marker‐based subpopulation, ie, assessment of the clinical validity of the marker. In this paper, we develop statistical testing strategies based on criteria that are explicitly designated to the marker assessment, including those examining treatment effects in marker‐negative patients. As existing and developed statistical testing strategies can assert treatment efficacy for either the overall patient population or the marker‐positive subpopulation, we also develop criteria for evaluating the operating characteristics of the statistical testing strategies based on the probabilities of asserting treatment efficacy across marker subpopulations. Numerical evaluations to compare the statistical testing strategies based on the developed criteria are provided.     

Blinded continuous monitoring in clinical trials with recurrent event endpoints

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.     

On the estimation of non linear functions in stochastic volatility models

Abstract

This paper focuses on the inference of suitable generally non linear functions in stochastic volatility models. In this context, in order to estimate the variance of the proposed estimators, a moving block bootstrap (MBB) approach is suggested and discussed. Under mild assumptions, we show that the MBB procedure is weakly consistent. Moreover, a methodology to choose the optimal length block in the MBB is proposed. Some examples and simulations on the model are also made to show the performance of the proposed procedure.     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

Tests for time series of counts based on the probability-generating function

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability-generating function computed from the observations. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is included as well as real-data examples.