Daily Inhalation Rate and Time‐Activity/Location Pattern in Japanese Preschool Children

Lack of data on daily inhalation rate and activity of children has been an issue in health risk assessment of air pollutants. This study aimed to obtain the daily inhalation rate and intensity and frequency of physical activity in relation to the environment in Japanese preschool children. Children aged four–six years (n= 138) in the suburbs of Tokyo participated in this study, which involved three days' continuous monitoring of physical activity using a tri‐axial accelerometer and parent's completion of a time/location diary during daily life. The estimated three‐day mean daily inhalation rate (body temperature, pressure, saturated with water vapor) was 9.9 ± 1.6 m³/day (0.52 ± 0.09 m³/kg/day). The current daily inhalation rate value of 0.580 m³/kg/day proposed for use in health risk assessment in Japan is confirmed to be valid to calculate central value of inhaled dose of air pollutants in five‐ to six‐year‐old children. However, the 95th percentile daily inhalation rate of 0.83 m³/kg/day based on measurement for five‐year‐old children is recommended to be used to provide an upper bound estimate of exposure that ensure the protection of all five‐ to six‐year‐old children from the health risk of air pollutants. Children spent the majority of their time in sedentary and light level of physical activity (LPA) when indoors, while 85% of their time when outdoors was spent in LPA and moderate‐to‐vigorous physical activity. The results suggest the need to consider variability of minute respiratory ventilation rate according to the environment for more refined short‐term health risk assessment.     

Earthquakes occurrences estimation through a parametric semi-Markov approach

The estimation of earthquakes’ occurrences prediction in seismic areas is a challenging problem in seismology and earthquake engineering. Indeed, the prevention and the quantification of possible damage provoked by destructive earthquakes are directly linked to this kind of prevision. In our paper, we adopt a parametric semi-Markov approach. This model assumes that a sequence of earthquakes is seen as a Markov process and besides it permits to take into consideration the more realistic assumption of events’ dependence in space and time. The elapsed time between two consecutive events is modeled as a general Weibull distribution. We determine then the transition probabilities and the so-called crossing states probabilities. We conclude then with a Monte Carlo simulation and the model is validated through a large database containing real data.     

Bivariate symbolic regression models for interval-valued variables

Interval-valued variables have become very common in data analysis. Up until now, symbolic regression mostly approaches this type of data from an optimization point of view, considering neither the probabilistic aspects of the models nor the nonlinear relationships between the interval response and the interval predictors. In this article, we formulate interval-valued variables as bivariate random vectors and introduce the bivariate symbolic regression model based on the generalized linear models theory which provides much-needed exibility in practice. Important inferential aspects are investigated. Applications to synthetic and real data illustrate the usefulness of the proposed approach.     

Bootstrap methods for bias correction and confidence interval estimation for nonlinear quantile regression of longitudinal data

This paper examines the use of bootstrapping for bias correction and calculation of confidence intervals (CIs) for a weighted nonlinear quantile regression estimator adjusted to the case of longitudinal data. Different weights and types of CIs are used and compared by computer simulation using a logistic growth function and error terms following an AR(1) model. The results indicate that bias correction reduces the bias of a point estimator but fails for CI calculations. A bootstrap percentile method and a normal approximation method perform well for two weights when used without bias correction. Taking both coverage and lengths of CIs into consideration, a non-bias-corrected percentile method with an unweighted estimator performs best.     

Testing for parameter constancy in the time series direction in panel data models

We propose tests for parameter constancy in the time series direction in panel data models. We construct a locally best invariant test based on Tanaka [Time series analysis: nonstationary and noninvertible distribution theory. New York: Wiley; 1996] and an asymptotically point optimal test based on Elliott and Müller [Efficient tests for general persistent time variation in regression coefficients. Rev Econ Stud. 2006;73:907–940]. We derive the limiting distributions of the test statistics as T→∞ while N is fixed, and calculate the critical values by applying numerical integration and response surface regression. Simulation results show that the proposed tests perform well if we apply them appropriately.     

On the survival time of a repairable duplex system sustained by a cold standby unit subjected to a priority rule

We analyze the survival time of a general duplex system sustained by a cold standby unit subjected to a priority rule. The analysis is based on advanced complex function theory (sectionally holomorphic functions). As an example, we consider Weibull–Gnedenko and Erlang distributions for failure and repair. Several graphs are displaying the survival function.     

基于多维效应的能源效率评估理论——以非参数DEA方法为例

基于多维效应的能源效率评估使人们在达到经济增长目的的同时兼顾能源安全、竞争力、环境、社会和技术的可持续性。论述能源效率内涵演化发展的路径，在此基础上将能源效率的内涵扩展至多维效应。以非参数DEA（Data Envelopment Analysis）方法为例，研究基于多维效应的能源效率评估方法的演化与发展趋势。研究结果显示:采用DEA方法对基于多维效应的能源效率进行评估的发展趋势主要有以下3点:能源效率的内涵不断延伸；DEA模型向非导向非径向模型转变；能源效率评估的研究维度不断扩展。对多维效应的能源效率综合指标评价体系和评价方法还需做深入研究。     

Opportunities and challenges of big data for the social sciences: The case of genomic data

In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large-scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research.     

Dependency and truncated forms of combinations in multivariate combination-based permutation tests and ordered categorical variables

ABSTRACT

Quite an important problem usually occurs in several multi-dimensional hypotheses testing problems when variables are correlated. In this framework the non-parametric combination (NPC) of a finite number of dependent permutation tests is suitable to cover almost all real situations of practical interest since the dependence relations among partial tests are implicitly captured by the combining procedure itself without the need to specify them [Pesarin F, Salmaso L. Permutation tests for complex data: theory, applications and software. Chichester: Wiley; 2010a]. An open problem related to NPC-based tests is the impact of the dependency structure on combined tests, especially in the presence of categorical variables. This paper’s goal is firstly to investigate the impact of the dependency structure on the possible significance of combined tests in cases of ordered categorical responses using Monte Carlo simulations, then to propose some specific procedures aimed at improving the power of multivariate combination-based permutation tests. The results show that an increasing level of correlation/association among responses negatively affects the power of combination-based multivariate permutation tests. The application of special forms of combination functions based on the truncated product method [Zaykin DV, Zhivotovsky LA, Westfall PH, Weir BS. Truncated product method for combining p-values. Genet Epidemiol. 2002;22:170–185; Dudbridge F, Koeleman BPC. Rank truncated product of p-values, with application to genomewide association scans. Genet Epidemiol. 2003;25:360–366] or on Liptak combination allowed us, using Monte Carlo simulations, to demonstrate the possibility of mitigating the negative effect on power of combination-based multivariate permutation tests produced by an increasing level of correlation/association among responses.