Distribution normality of pH and H<Superscript>+</Superscript> activity in soil

Soil pH, a log-transformed form of hydrogen ion (H⁺) activity, is often used for various statistics. However whether soil pH meets the requirement of distribution normality for statistical analyses is unknown. We studied the statistical suitability of both soil pH and H⁺ activity by examining their distribution normalities. Results show that the variability of H⁺ activity is higher and its distribution is skewer, which is significantly different from the normal and soil pH, regardless of soil conditions and analytical methods. Here we demonstrate that soil pH is more appropriate than the H⁺ activity for performing statistical analysis in terms of distribution normality.     

基于Client/Server数据完整性约束之研究

本文主要讨论基于Client/Server数据完整性约束及其如何实施企业业务规则,并以SQLServer和PowerBuilder为例,介绍了数据完整性约束的实现技术.     

Bayesian testing for trend in a power model for clustered binary data

Developmental toxicity studies are widely used to investigate the potential risk of environmental hazards. In dose–response experiments, subjects are randomly allocated to groups receiving various dose levels. Tests for trend are then often applied to assess possible dose effects. Recent techniques for risk assessment in this area are based on fitting dose–response models. The complexity of such studies implies a number of non-trivial challenges for model development and the construction of dose-related trend tests, including the hierarchical structure of the data, litter effects inducing extra variation, the functional form of the dose–response curve, the adverse event at dam or at fetus level, the inference paradigm, etc. The purpose of this paper is to propose a Bayesian trend test based on a non-linear power model for the dose effect and using an appropriate model for clustered binary data. Our work is motivated by the analysis of developmental toxicity studies, in which the offspring of exposed and control rodents are examined for defects. Simulations show the performance of the method over a number of samples generated under typical experimental conditions.     

Measures of preferred direction for environmental and ecological circular data

Circular or angular variables indicating direction or cyclical time can be of great interest to scientists studying ecology, biology or environmental issues. A common problem of interest in circular data is estimating a preferred direction and its corresponding distribution. This problem is complicated by the so-called “wrap-around effect” on the circle, which exists because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional data, as they do not account for its circular nature. Three choices for summarizing the preferred direction (the sample circular mean, sample circular median and a circular analog of the Hodges–Lehmann estimator) are discussed, with examples from environmental and ecological applications. Similar to the linear data case, the relative emphases of different methods sometimes yield different measures of preferred direction in the presence of outliers or lack of symmetry in the original data. Received: November 2003 / Revised: June 2004     

Modelling the response of tree growth to temperature and CO2 elevation as related to the fertility and current temperature sum of a site

A methodology for simulating climate change impacts on tree growth was introduced into a statistical growth and yield model in relation to variations in site fertility and location implemented with current temperature sum. This was based on a procedure in which the relative enhancement in stem volume growth was calculated from short-term runs of a physiological simulation model for Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and silver birch (Betula pendula Roth.) stands. These simulations were made for a set of stands with species-specific variations in stand characteristics, location and fertility type first in current climatic conditions and then in different combinations of CO₂ and temperature elevations. Based on these simulations, the relative enhancement of volume growth induced by the climate change (relative scenario effect, RSEv) was calculated and modelled in relation to: (i) CO₂ and temperature elevation, stand density and the competition status of the tree in its stand, and (ii) variations in site fertility type and current temperature sum of a stand. Finally, these transfer functions for RSEv were applied to adapt the stem volume growth in the statistical growth and yield model to reflect the response to climate change.     

Robust benchmark dose determination based on profile score methods

We investigate several methods commonly used to obtain a benchmark dose and show that those based on full likelihood or profile likelihood methods might have severe shortcomings. We propose two new profile likelihood-based approaches which overcome these problems. Another contribution is the extension of the benchmark dose determination to non full likelihood models, such as quasi-likelihood, generalized estimating equations, which are widely used in settings such as developmental toxicity where clustered data are encountered. This widening of the scope of application is possible by the use of (robust) score statistics. Benchmark dose methods are applied to a data set from a developmental toxicity study.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

On thresholds and environmental curve tensiometers

This paper considers distinctions between lognormal and mixture models. Emphasis is placed on two component mixtures where the lower valued subpopulation has a large mixing parameter. The density of this sort of mixture can be easily mistaken for a lognormal density. In order to compare such a mixture to a lognormal it is demonstrated that Galton's two parameter logmodel and Pearson's five parameternormal mixture are special, or limiting, cases of the same general mixture model. Consideration is given to the lognormal threshold parameter in order to devise a tool that can help distinguish mixtures from lognormals. Based on the threshold parameter, piloted procedures can help measure whether or not a curve is friable, in the sense that a brittle curve is better represented as a mixture than as a skewed lognormal. It is also shown that generalizations of Galton's product risk model can be represented interms of the threshold parameter Based on a tool called a curve tensiometer was designed to be applied as a graphical friability check in the ecological context of Fisher's classic Iris data and in the environmental context of a Santa Monica Bay fish consumption study.     

Statistics: an essential technology in environmental research and management

Statistical methods as developed and used in decision making and scientific research are of recent origin. The logical foundations of statistics are still under discussion and some care is needed in applying the existing methodology and interpreting results. Some pitfalls in statistical data analysis are discussed and the importance of cross examination of data (or exploratory data analysis) before using specific statistical techniques are emphasized. Comments are made on the treatment of outliers, choice of stochastic models, use of multivariate techniques and the choice of software (expert systems) in statistical analysis. The need for developing new methodology with particular relevance to environmental research and policy is stressed.Dr Rao is Eberly Professor of Statistics and Director of the Penn State Center for Multivariate Analysis. He has received PhD and ScD degrees from Cambridge University, and has been awarded numerous honorary doctorates from universities around the world. He is a Fellow of Royal Society, UK; Fellow of Indian National Science Academy; Foreign Honorary Member of American Academy of Arts and Science; Life Fellow of King's College, Cambridge; and Founder Fellow of the Third World Academy of Sciences. He is Honorary Fellow and President of International Statistical Institute, Biometric Society and elected Fellow of the Institute of Mathematical Statistics. He has made outstanding contributions to virtually all important topics of theoretical and applied statistics, and many results bear his name. He has been Editor of Sankhya and theJournal of Multivariate Analysis, and serves on international advisory boards of several professional journals, includingEnvironmetrics and theJournal of Environmental Statistics. This paper is based on the keynote address to the Seventh Annual Conference on Statistics of the United States Environmental Protection Agency.     

关于提升三级环境监测站监测数据地位的几点思考

本文针对目前环境监测数据方面存在的问题，提出了如何提高环境监测数据地位的建议。