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We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the purpose in hand, but also performs very well, and avoids the tendency to under‐smooth or to interpolate the data that can occur with other data‐driven methods used to choose the smoothing parameter. The method is applied to data from a chemical process and to stream salinity measurements. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Estimation of ocean environmental return values is critical to the safety and reliability of marine and coastal structures. For ocean waves and storm severity, return values are typically estimated by extreme value analysis of time series of measured or hindcast sea state significant wave height HS. For a single location, this analysis is complicated by the serial dependence of HS in time and its non‐stationarity with respect to multiple covariates, particularly direction and season. Here, we report a non‐stationary extreme value analysis of storm peak significant wave height , assumed temporally independent given covariates, incorporating directional and seasonal effects using a spline‐based methodology incorporating an ensemble of models for different extreme value thresholds. Quantile regression is used to estimate suitable thresholds. For each threshold, a Poisson process is used to estimate the rate of occurrence of threshold exceedances, and a generalised Pareto model characterises the magnitude of threshold exceedances. Covariate effects are incorporated at each stage using penalised tensor products of B‐splines to give smooth model parameter variation as a function of covariates. Optimal smoothing penalties are selected using cross‐validation, and uncertainty is quantified using bias‐corrected and accelerated bootstrap resampling. We use the model to estimate environmental return values for a location in the Makassar Strait, in the South China Sea. Return values distributions for are estimated by simulation under the threshold ensemble model. Return values for HS are then estimated by simulating intra‐storm trajectories of HS consistent with the characteristics of the simulated storm peak events using a matching procedure. Return values for maximum individual crest elevation C are estimated by marginalisation using a pre‐specified conditional distribution for C given HS and other sea state parameters. Model validation is performed by comparing confidence intervals for cumulative distribution functions of and HS for the period of the data with empirical sample‐based estimates. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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We propose a simple piecewise model for a sample of peaks‐over‐threshold, nonstationary with respect to multidimensional covariates, and estimate it using a carefully designed and computationally efficient Bayesian inference. Model parameters are themselves parameterized as functions of covariates using penalized B‐spline representations. This allows detailed characterization of non‐stationarity extreme environments. The approach gives similar inferences to a comparable frequentist penalized maximum likelihood method, but is computationally considerably more efficient and allows a more complete characterization of uncertainty in a single modelling step. We use the model to quantify the joint directional and seasonal variation of storm peak significant wave height at a northern North Sea location and estimate predictive directional–seasonal return value distributions necessary for the design and reliability assessment of marine and coastal structures. 相似文献
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Ecological studies involving counts of abundance, presence–absence or occupancy rates often produce data having a substantial proportion of zeros. Furthermore, these types of processes are typically multivariate and only adequately described by complex nonlinear relationships involving externally measured covariates. Ignoring these aspects of the data and implementing standard approaches can lead to models that fail to provide adequate scientific understanding of the underlying ecological processes, possibly resulting in a loss of inferential power. One method of dealing with data having excess zeros is to consider the class of univariate zero‐inflated generalized linear models. However, this class of models fails to address the multivariate and nonlinear aspects associated with the data usually encountered in practice. Therefore, we propose a semiparametric bivariate zero‐inflated Poisson model that takes into account both of these data attributes. The general modeling framework is hierarchical Bayes and is suitable for a broad range of applications. We demonstrate the effectiveness of our model through a motivating example on modeling catch per unit area for multiple species using data from the Missouri River Benthic Fishes Study, implemented by the United States Geological Survey. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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The existence of multiple environmental hazards is obviously a threat to human health and, from a statistical point of view, the modeling and the detection of disease clusters potentially related to those hazards offer challenging tasks. In this paper, we consider low rank thin plate spline (TPS) models within a semiparametric approach to focused clustering for small area health data. Both the distance from a putative source and a general, unspecified clustering process are modeled in the same fashion and they are entered log‐additively in mixed Poisson‐Normal models. Some issues related to the identification of the random effects arising from this approach are investigated. Under different simulated scenarios, we evaluate the proposed models using conditional Akaike's weights and tests for variance components, providing a comprehensive model selection methodology easy to implement. We examine observations of lung cancer deaths taken in Ohio between 1987 and 1988. These data were analyzed on several occasions to investigate the risk associated with a putative source in Hamilton county. In our analysis, we found a strong south‐eastward spatial trend which is confounded with a significant radial distance effect decreasing between 0 and 150 km from the point source. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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朱辉 《防灾减灾工程学报》2012,(5):45-47
针对宁夏中宁发电有限责任公司斗轮堆取料机(简称斗轮机)斗轮轴胀套联接处粘死问题,进行分析、计算,将胀套联接方式改为花键联接方式。结果表明:斗轮机长期在超负荷工况下运行是造成斗轮轴胀套联接处粘死的主要原因;联接方式改进后,斗轮轴传动负荷能力增大,检修方便,达到了预期的改造效果。 相似文献
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虹膜识别在肉类食品安全追溯系统中的应用及关键技术研究 总被引：4，自引：2，他引：4
为了提高大型动物肉类食品的安全性,在研究肉类食品供应链的安全管理的基础上,将大型动物的虹膜识别技术引入食品供应链管理中,构建基于虹膜识别的大型动物肉类食品安全可追溯系统,达到肉类食品安全信息查询及出现安全问题后的个体溯源的目的.借鉴人眼虹膜识别的相关技术,以牛眼为例对大型动物虹膜识别的相关算法和虹膜定位等关键技术进行研究,运用改进的Sobel算子对图像进行边缘检测;引入二次B样条曲线算法实现了牛眼虹膜的精确定位;分析牛虹膜纹理特征的分布特点,实现了虹膜特征区域的提取及归一化处理.研究成果对大型动物的虹膜识别研究和肉类食品供应链安全管理具有一定的参考意义. 相似文献
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Water deposition of pollutants can be a good indicator of both air and water quality in a region of interest. In this paper, we study sulphate deposition change over time in a network of multiple monitoring stations in the Turkey Lakes Watershed in Sault Ste. Marie, Ontario, Canada. As there is generally substantial correlation among sulphate deposition observed over time and space, we incorporate temporally correlated multivariate random effects into Gamma regression models to account for the temporal dependence within each station and between‐station dependence in space. We applied our new approach to analyse monthly average sulphate depositions between 1983 and 2003. We found the observed increase of sulphate deposition between 1994 and 2003 was not significant, that is, annual trends in sulphate deposition had stabilized since 1994. Our analysis also quantified increasing sulphate deposition from upstream to downstream and its monthly fluctuations from higher in winter to lower in summer. Understanding of these sulphate deposition trends is of great policy relevance to environmental conservation. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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目的 研究区域气压、温度、湿度等典型环境因素历史数据的精确预测方法。方法 通过分析气压的时空变化规律,采用样条函数拟合气压在时间维度上的变化规律,利用高斯分布刻画不同站点间空间上的相关性,构建区域历史气压数据时空分布模型。结果 模型对海南岛某站气压数据拟合值与实测值的最大绝对误差不超过0.8 hPa,平均绝对误差不超过0.2 hPa。模型对海南岛某站气压的预测平均绝对误差在6 hPa以下(占比95.3%),对高原地区某站气压的预测平均绝对误差约为29.5 hPa,平均相对误差为4.6%,预测精度较高。结论 采用构建的时空分布模型能够为区域环境因素极值计算提供更多的基础数据,该方法还可以广泛应用于绝对湿度、温度等环境因素边界条件的预测。 相似文献