An Improved Framework for Uncertainty Analysis: Accounting for Unsuspected Errors

I use an analogy with the history of physical measurements, population and energy projections, and analyze the trends in several data sets to quantify the overconfidence of the experts in the reliability of their uncertainty estimates. Data sets include (i) time trends in the sequential measurements of the same physical quantity; (ii) national population projections; and (iii) projections for the U.S., energy sector. Probabilities of large deviations for the true values are parametrized by an exponential distribution with the slope determined by the data. Statistics of past errors can be used in probabilistic risk assessment to hedge against unsuspected uncertainties and to include the possibility of human error into the framework of uncertainty analysis. By means of a sample Monte Carlo simulation of cancer risk caused by ingestion of benzene in soil, I demonstrate how the upper 95th percentiles of risk are changed when unsuspected uncertainties are included. I recommend to inflate the estimated uncertainties by default safety factors determined from the relevant historical data sets.     

Measurements of separation among probability densities or random variables

Distance between two probability densities or two random variables is a well established concept in statistics. The present paper considers generalizations of distances to separation measurements for three or more elements in a function space. Geometric intuition and examples from hypothesis testing suggest lower and upper bounds for such measurements in terms of pairwise distances; but also in L_p spaces some useful non-pairwise separation measurements always lie within these bounds. Examples of such separation measurements are the Bayes probability of correct classification among several arbitrary distributions, and the expected range among several random variables.     

Robust estimation of location in samples of size three

Youden (1953) discussed the practice of averaging the two most concordant observations in sets of three measurements as a method of estimating location. Distributional results for this estimator can be found in Seth (1950) and Lieblein (1952). It follows from their work that the sample median has smaller variance for normal and uniform populations. In this paper it is shown that themedian stochastically dominates the average of the two closest observations for uniform, normal, double–exponential and Cauchy populations and thus is the superior resistant estimator in these cases for a broad class of loss functions. However, an example is given in which, for a particular contaminaion model and loss function, the mean of the closest two observations has smaller risk than the median.     

Arsenic exposure associated with decreased bone mineralization in male

Abstract

Objective: Arsenic (As) exposure may cause several medical problems. There were a few studies investigated whether it has affected bone tissue in women. However, there was no study in men. The aim of this study was to evaluate associations between bone mineral density (BMD) and As exposure in men subjects.

Material and methods: We enrolled in this study 254 subjects who due to chronic As exposure suspected and 82 subjects as a control group. Hair As levels were detected by a hair analysis (Varian AA240Z Zeeman Atomic Absorption Spectrometer, USA). BMD measurements were obtained using dual-energy X-ray absorptiometry instrumentation. We investigated associations between the hair As levels and BMD measurements.

Results: The frequency of osteoporosis and osteopenia was found to be 0.8% and 54.5%, respectively, in the As exposure group. The frequency of osteoporosis was found to be 1% and osteopenia was 32.4% in control subjects. There was significant difference between two groups (p?<?0.001). Hair As level has a median 1.01 (min: 0.06 and max: 25.71). There were no significant correlation between hair As levels and BMD measurements.

Conclusion: According to our observations, As exposure was associated with bone metabolism. Possible cause of osteopenia may be exposure to As. Further investigations are needed to estimate the relationship between As and bone metabolism.     

用补偿法测量人体深部温度

本文讨论了在人体表面测量深部体温的补偿测温方法。根据生物体导热方程,分析了人体组织的生理参数及测温探头的几何参数对测量结果的影响。据此制作了测温探头,进行了测温试验,得到了初步的试验结果。     

TABLES OF SMALL OPTIMAL REPEATED MEASUREMENTS DESIGNS

This paper presents tables of the optimal repeated measurements designs for the estimation of direct effects and of residual effects for a model with independent errors, for up to n = 10 experimental units, for t= 2 treatments and p= 2, 3 or 4 periods, and for t= 3 treatments and p= 2 or 3 periods.     

天线方向图的中场测量和远场变换

提出一种测量大口径微波天线方向图的新方法,该方法通过中场幅相数据采集和复源点远场变换,可利用现有场地和仪器测量大天线的远场方向图。     

Functional measurement error in functional regression

Measurement error is an important problem that has not been studied very well in the context of functional data analysis. To the best of our knowledge, there are no existing methods that address the presence of functional measurement errors in generalized functional linear models. In this article, a novel approach is proposed to estimate the slope function in the presence of measurement error in the generalized functional linear model with a scalar response. This work significantly advances the existing conditional score method to accommodate the case where both the measurement error and independent variables lie in infinite dimensional spaces. Asymptotic results are established for the proposed estimate, and its behaviour is studied via simulations, where the response is continuous or binary. Analysis of Canadian Weather data highlights the practical utility of our method. The Canadian Journal of Statistics 48: 238–258; 2020 © 2020 Statistical Society of Canada     

Analysis of longitudinal data with non-ignorable non-monotone missing values

A full likelihood method is proposed to analyse continuous longitudinal data with non-ignorable (informative) missing values and non-monotone patterns. The problem arose in a breast cancer clinical trial where repeated assessments of quality of life were collected: patients rated their coping ability during and after treatment. We allow the missingness probabilities to depend on unobserved responses, and we use a multivariate normal model for the outcomes. A first-order Markov dependence structure for the responses is a natural choice and facilitates the construction of the likelihood; estimates are obtained via the Nelder–Mead simplex algorithm. Computations are difficult and become intractable with more than three or four assessments. Applying the method to the quality-of-life data results in easily interpretable estimates, confirms the suspicion that the data are non-ignorably missing and highlights the likely bias of standard methods. Although treatment comparisons are not affected here, the methods are useful for obtaining unbiased means and estimating trends over time.     

Modelling spatio-temporal air pollution data from a mobile monitoring station

ABSTRACT

Environmental data is typically indexed in space and time. This work deals with modelling spatio-temporal air quality data, when multiple measurements are available for each space-time point. Typically this situation arises when different measurements referring to several response variables are observed in each space-time point, for example, different pollutants or size resolved data on particular matter. Nonetheless, such a kind of data also arises when using a mobile monitoring station moving along a path for a certain period of time. In this case, each spatio-temporal point has a number of measurements referring to the response variable observed several times over different locations in a close neighbourhood of the space-time point. We deal with this type of data within a hierarchical Bayesian framework, in which observed measurements are modelled in the first stage of the hierarchy, while the unobserved spatio-temporal process is considered in the following stages. The final model is very flexible and includes autoregressive terms in time, different structures for the variance-covariance matrix of the errors, and can manage covariates available at different space-time resolutions. This approach is motivated by the availability of data on urban pollution dynamics: fast measures of gases and size resolved particulate matter have been collected using an Optical Particle Counter located on a cabin of a public conveyance that moves on a monorail on a line transect of a town. Urban microclimate information is also available and included in the model. Simulation studies are conducted to evaluate the performance of the proposed model over existing alternatives that do not model data over the first stage of the hierarchy.