Poverty statics and dynamics: does the accounting period matter?

Any public policy aimed at reducing the effects of deprivation should be aware of the consequences of the use of different income accounting periods on eligibility and subsequent policy evaluation. This study aims to throw some light on the relev-ance of choosing a specific accounting period for public policy evaluation in a European country, in contrast to the existing evidence for the United States. Our analysis indicates that there are some significant differences in the results on poverty when using different income accounting periods. Researchers and decision-makers using an annual income measure, whatever the economic and social welfare context, should be aware that the use of quarterly data instead of yearly data will lead to a significantly larger number of poor households and a lower level of inequality and mobility in the distribution.     

Modelling the effects of repellent chemicals on foraging bees

Summary.  Many pesticide sprays that are used for crop protection are harmful to honey-bees. It can therefore be beneficial to add to the spray chemical compounds that are repellent to bees, to discourage them from feeding on recently sprayed crops. Experiments were conducted using an artificial feeding station to assess the repellent effects of various compounds. In this system, bees arrive at the feeding station, choose between feeding dishes to which different chemicals have been added, feed for a variable period and then depart. The number of bees at each feeding dish is recorded at intervals of 1 min. We discuss the analysis of data from this type of experiment, based on a queuing theory model.     

Estimation of Polygons and Areas

Abstract.  In forestry the problem of estimating areas is central. This paper addresses area estimation through fitting of a polygon to observed coordinate data. Coordinates of corners and points along the sides of a simple closed polygon are measured with independent random errors. This paper focuses on procedures to adjust the coordinates for estimation of the polygon and its area. Different new techniques that consider different amounts of prior information are described and compared. The different techniques use restricted least squares, maximum likelihood and the expectation maximization algorithm. In a simulation study it is shown that the root mean square errors of the estimates are decreased when coordinates are adjusted before estimation. Minor further improvement is achieved by using prior information about the order and the distribution of the points along the sides of the polygon. This paper has its origin in forestry but there are also other applications.     

Conservative prior distributions for variance parameters in hierarchical models

Bayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for “default” priors in the literature, often a conditionally conjugate inverse‐gamma specification is used, despite documented drawbacks of this choice. The authors suggest “conservative” prior distributions for variance components, which deliberately give more weight to smaller values. These are appropriate for investigators who are skeptical about the presence of variability in the second‐stage parameters (random effects) and want to particularly guard against inferring more structure than is really present. The suggested priors readily adapt to various hierarchical modelling settings, such as fitting smooth curves, modelling spatial variation and combining data from multiple sites.     

A hierarchical model for extreme wind speeds

Summary.  A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.     

Acceptance sampling based on truncated life tests for generalized Rayleigh distribution

This paper considers the problem of an acceptance sampling plan for a truncated life test when the lifetime follows the generalized Rayleigh distribution. For different acceptance numbers, confidence levels, and values of the ratio of the fixed experiment time to the specified mean life, the minimum sample sizes necessary to ensure the specified mean life are found. The operating characteristic values of the sampling plans and producer's risk are discussed. Some tables are presented and the use of the tables is illustrated by a numerical example.     

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

Abstract.  The Andersson–Madigan–Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced Lauritzen–Wermuth–Frydenberg Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.     

Identifiability of Finite Mixtures of Elliptical Distributions

Abstract.  We present general results on the identifiability of finite mixtures of elliptical distributions under conditions on the characteristic generators or density generators. Examples include the multivariate t -distribution, symmetric stable laws, exponential power and Kotz distributions. In each case, the shape parameter is allowed to vary in the mixture, in addition to the location vector and the scatter matrix. Furthermore, we discuss the identifiability of finite mixtures of elliptical densities with generators that correspond to scale mixtures of normal distributions.     

A robust prediction error criterion for pareto modelling of upper tails

Estimation of the Pareto tail index from extreme order statistics is an important problem in many settings. The upper tail of the distribution, where data are sparse, is typically fitted with a model, such as the Pareto model, from which quantities such as probabilities associated with extreme events are deduced. The success of this procedure relies heavily not only on the choice of the estimator for the Pareto tail index but also on the procedure used to determine the number k of extreme order statistics that are used for the estimation. The authors develop a robust prediction error criterion for choosing k and estimating the Pareto index. A Monte Carlo study shows the good performance of the new estimator and the analysis of real data sets illustrates that a robust procedure for selection, and not just for estimation, is needed.     

名词分布是人类语言的不变量吗？——以德语书面语中名词分布为例

此前对人类自然语言中词类分布的研究显示，不同语言中名词所占比例相对固定。德语中名词所占比例是否也符合这一普遍规律？通过对三个大型德语语料库进行研究发现：首先，德语书面语中的名词占比约为38%，尽管德语复合名词比例高、名词化结构多，但其名词占比同英语以及其他语言中的名词占比大致相当，从而进一步证实了人类自然语言中名词占比具有普遍规律这一结论；其次，不同文体中名词及其各子类的占比有所差异，而这一差异由文体特征决定，并且具有跨语言的相似性；最后，时间因素与文体类型均对名词各个子类占比有显著影响，但名词总体占比未受二者影响。综上，可以进一步证实名词分布是人类语言的不变量这一结论。