Multivariate bootstrap confidence regions for abundance vector using

Abundance vector estimation is a well investigated problem in statistical ecology. The use of simple random sampling with replacement or replicated sampling ensures good asymptotic properties of the abundance vector estimators. However, real surveys are based on small sample sizes, and assuming any specific distribution of the abundance vector estimator may be hazardous.In this paper we focus our attention on situations where the population is not too large and the sample size is small. We propose bootstrap multivariate confidence regions based on data depth. Data depth is a geometrical concept of ordering data from the center outwardly in higher dimensions. The Simplicial depth, the Tukey's depth and the Mahalanobis depth are presented. In order to build confidence regions in the presence of a skewed distribution of the abundance vector estimator, the use of Tukey's depth is suggested. The proposed method has been applied to the benthic community of Lake Lesina. A comparison with Mahalanobis depth and standard existing methods is reported.     

A capture-recapture model with heterogeneity and behavioural response

We develop the non-parametric maximum likelihood estimator (MLE) of the full M_bh capture-recapture model which utilizes both initial capture and recapture data and permits both heterogeneity (h) between animals and behavioural (b) response to capture. Our MLE procedure utilizes non-parametric maximum likelihood estimation of mixture distributions (Lindsay, 1983; Lindsay and Roeder, 1992) and the EM algorithm (Dempsteret al., 1977). Our MLE estimate provides the first non-parametric estimate of the bivariate capture-recapture distribution.Since non-parametric maximum likelihood estimation exists for submodels M_h (allowing heterogeneity only), Mb (allowing behavioural response only) and M₀ (allowing no changes), we develop maximum likelihood-based model selection, specifically the Akaike information criterion (AIC) (Akaike, 1973). The AIC procedure does well in detecting behavioural response but has difficulty in detecting heterogeneity.     

黄海西部降水pH值的年度及季节统计特征

采用内核密度估计方法对黄海西部降水pH值年度/季节特征进行了分析.结果显示,降水pH值年度内核密度估计图相似,呈典型双态分布,以低pH值峰为主.季节性分布特征春、夏、冬季相近,低pH值峰为主,秋季高pH值峰为主.该海域降水pH值特征是陆源输入与海源输入相互影响的结果.根据数据非正态分布特点,采用bootstrap模拟取样获得的降水pH代表值显示,黄海西部降水总体呈弱酸雨特征,年度与季节性pH代表值都呈弱酸性,且年度降水pH值变化范围要大于季节性变化,降水酸性依次为春季>冬季>秋季>夏季,季节性特征较显著.      

A COMPARISON OF METHODS FOR ESTIMATING LOW FLOW CHARACTERISTICS OF STREAMS1

ABSTRACT: Four methods for estimating the 7-day, 10-year and 7-day, 20-year low flows for streams are compared by the bootstrap method. The bootstrap method is a Monte Carlo technique in which random samples are drawn from an unspecified sampling distribution defined from observed data. The nonparametric nature of the bootstrap makes it suitable for comparing methods based on a flow series for which the true distribution is unknown. Results show that the two methods based on hypothetical distributions (Log-Pearson III and Weibull) had lower mean square errors than did the Box-Cox transformation method or the Log-Boughton method which is based on a fit of plotting positions.     

确定湖泊参照状态过程中频率分析的精度估计

确定湖泊参照状态是建立湖泊水质基准的关键步骤之一。以频率分析为基础的方法,如湖泊群体分布法、频率分析法、三分法等广泛地应用于参照状态的研究中;但是,由于湖泊观测数据具有关联性以及难以确定概率分布,这些研究都未给出参照状态估计的置信区间。滑块自助法无需确定观测数据的理论概率分布,同时能很好地克服数据关联性引起的问题,给出这些方法得到的参照状态的置信区间。以太湖为例,分析了确定频率分析过程中,正态分布法和普通自助法的缺陷;结果说明这一方法适合于确定湖泊参照状态的精度。     

Nexus between renewable energy consumption,economic growth,and CO2 emissions in Algeria: New evidence from the Fourier-Bootstrap ARDL approach

Governments often impose new energy strategies to support new CO₂ emission-reducing technologies without affecting economic growth. Hence, this study aims to re-investigate the relationship between economic growth, renewable energy use, and CO₂ emissions in Algeria from 1990 to 2018. Motivated by the mixed findings of the existing literature, which ignore the Fourier function and bootstrap test and apply the newly developed Fourier bootstrap autoregressive distributed lag model (FARDL). Our findings indicate that renewable energy use and growth have a long-run relationship with CO₂ emissions and do not accept the existence of the Environmental Kuznets Curve (EKC) hypothesis for CO₂ emissions in Algeria. In the long term, the results show that renewable energy use has a negative and significant impact, and growth has a positive and statistically significant effect on CO₂ emissions. In the short run, the findings indicate that renewable energy use reduces CO₂ emissions, while both the growth and squared growth had positive and statistically insignificant impacts on CO₂ emissions, confirming the lack of evidence supporting the EKC hypothesis. Moreover, the causality test indicates a one-way causation from growth to renewable energy use, confirming the conservation hypothesis for Algeria and from growth to CO₂ emissions. Interestingly, we found one-way causality from CO₂ emissions to renewable energy use, attributing this to the fact that renewable energy usage has yet to reach a point that it can significantly cause a CO₂ emissions reduction. Based on the results, we recommend that policymakers design appropriate policies to decarbonize energy consumption, e.g., increasing fossil fuel costs and implementing a carbon tax. In contrast, Algeria should promote new CO₂ emission-reducing technologies without affecting economic growth, e.g., tax exemptions and reductions for enterprise owners in the renewable energy industry.