Restricted adaptive cluster sampling

Adaptive cluster sampling can be a useful design for sampling rare and patchy populations. With this design the initial sample size is fixed but the size of the final sample (and total sampling effort) cannot be predicted prior to sampling. For some populations the final sample size can be quite variable depending on the level of patchiness. Restricted adaptive cluster sampling is a proposed modification where a limit is placed on the sample size prior to sampling and quadrats are selected sequentially for the initial sample size. As a result there is less variation in the final sample size and the total sampling effort can be predicted with some certainty, which is impor- tant for many ecological studies. Estimates of density are biased with the restricted design but under some circumstances the bias can be estimated well by bootstrapping. © Rapid Science 1998     

Using the bootstrap with two-phase adaptive stratified samples from multiple populations at multiple locations

Recently the two-phase adaptive stratified sampling design proposed by Francis (1984) has been extended by Manly et al. (2002) for situations where several biological populations are sampled simultaneously, and where this is done at several different geographical locations in order to estimate population totals or means. The method uses the results from a first phase sample to decide how best to allocate a second phase sample to locations and strata, in order to maximise a criterion (based on estimated coefficients of variation) that measures the accuracy of estimation for population totals, for all variables at all locations. One potential problem with this method is bias in the estimators of the population totals and means. In this paper bootstrapping is considered as a means of overcoming these biases. It is shown using model populations of Pacific walrus and shellfish, based on real data, that bootstrapping is a useful tool for removing about half of the bias. This is also confirmed from some simulations using artificial data.     

A nonparametric procedure for analyzing repeated measures of spatially correlated data

Many agricultural, biological, and environmental studies involve detecting temporal changes of a response variable, based on data observed at sampling sites in a spatial region and repeatedly over several time points. That is, data are repeated measures over time and are potentially correlated across space. The traditional repeated-measures analysis allows for time dependence but assumes that the observations at different sampling sites are mutually independent, which may not be suitable for field data that are correlated across space. In this paper, a nonparametric large-sample inference procedure is developed to assess the time effects while accounting for the spatial dependence using a block bootstrap. For illustration, the methodology is applied to describe the population changes of root-lesion nematodes over time in a production field in Wisconsin.     

Resampling methods for estimating dispersion indices in random and adaptive designs

In many surveys in environmental and natural phenomena the aim is to evaluate the heterogeneity, and the skewness of the distribution of the number point-objects in the study area opportunely partitioned in sub-regions. For this purpose, in this paper the estimation of dispersion indices is considered by using simple random sampling and adaptive sampling with initial simple random sampling selected with replacement or without replacement. The jackknife and the bootstrap procedures are proposed in both cases for reducing bias. Finally, both a simulation study and a case study on biological population referred to a Oidium tuckeri contamination in a growing vineyard is performed to assess the accuracy of the proposed estimators.     

整合分析中的非参数检验:重复取样检验法的实例应用

整合分析(meta-analysis)是对同一主题下多个独立实验结果进行综合的统计学方法。非参数检验整合分析——重复取样检验(resampling test)不考虑原文献数据的分布形式，故可在不知原文献数据分布形式时使用。其中的靴襻法(bootstrap)可用来给出总效应值的置信区间，但不能检验组内异质性是否显著。靴襻法与随机检验法(randomization test)可以有效弥补这一缺失，判断出组间差异性是否显著。实例应用表明，重复取样检验没有参数检验保守，又与参数检验的结果差异较小。     

ESTIMATION OF LONG-TERM DISCHARGE STATISTICS BY REGIONAL ADJUSTMENT1

ABSTRACT: A regional adjustment relationship was developed to estimate long-term (30-year) monthly median discharges from short term (three-year) records. This method differs from traditional approaches in that it is based on site-specific discharge data but does not require correlation of these data with discharges from a single hydrologically similar long-term gage. The method is shown to be statistically robust, and applicable to statistics other than the median.     

Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression

We discuss a method for analyzing data that are positively skewed and contain a substantial proportion of zeros. Such data commonly arise in ecological applications, when the focus is on the abundance of a species. The form of the distribution is then due to the patchy nature of the environment and/or the inherent heterogeneity of the species. The method can be used whenever we wish to model the data as a response variable in terms of one or more explanatory variables. The analysis consists of three stages. The first involves creating two sets of data from the original: one shows whether or not the species is present; the other indicates the logarithm of the abundance when it is present. These are referred to as the presence data and the log-abundance data, respectively. The second stage involves modelling the presence data using logistic regression, and separately modelling the log-abundance data using ordinary regression. Finally, the third stage involves combining the two models in order to estimate the expected abundance for a specific set of values of the explanatory variables. A common approach to analyzing this sort of data is to use a ln (y+c) transformation, where c is some constant (usually one). The method we use here avoids the need for an arbitrary choice of the value of c, and allows the modelling to be carried out in a natural and straightforward manner, using well-known regression techniques. The approach we put forward is not original, having been used in both conservation biology and fisheries. Our objectives in this paper are to (a) promote the application of this approach in a wide range of settings and (b) suggest that parametric bootstrapping be used to provide confidence limits for the estimate of expected abundance.     

Bootstrap方法在小样本随机振动环境测量数据归纳中的应用研究

针对传统正态容差上限方法在确定平台振动环境条件时,不适合处理小样本实测数据统计归纳的问题,提出了利用Bootstrap法对测量样本重采样,进而统计归纳振动环境条件的计算方法。首先利用Bootstrap法对来自正态分布的试验数据进行容差上限估计,通过与真值的对比分析,验证各种自助置信区间估计方法的适用性,而后提出使用纠偏百分位法和Bootstrap-t法对小样本振动测量数据进行归纳。最后,分别对某型飞机实测数据进行了统计归纳,并与传统容差上限估计方法进行了对比分析。     

中国火电企业生产效率评价与影响因素研究

基于2008~2017年省级统计数据,研究中国火电企业生产效率与影响因素.首先,运用Bootstrap方法修正的DEA模型研究火电企业静态效率;其次,结合全局参比Malmquist指数的可传递性研究火电企业动态效率;最后,运用Tobit模型研究火电企业生产效率影响因素,基于稳健性考虑运用固定效应模型验证检验结果的可靠性.效率评价实证结果显示：2008~2017年无一省（自治区、直辖市）的火电企业生产效率处于生产前沿面,也无一省（自治区、直辖市）的火电企业生产效率持续提高.2008~2013年全国火电企业生产效率比较平稳,2013~2016年呈下降趋势且省际差异逐步扩大,2017年有所回升.2008~2017年东中西三大地带火电企业效率和平稳性呈现东部最优、中部次之、西部最差的格局.影响因素实证结果显示：设备性能、经济发展速度、市场竞争程度是火电企业生产效率的显著影响因素;政府对环境污染重视程度和碳排放权交易市场对火电企业生产效率影响有限.     

A weight-adjusted Peto’s test when cause of death is not assigned

A new statistical testing approach using a weighted logrank statistic is developed for rodent tumorigenicity assays that have a single terminal sacrifice but not cause-of-death data. Instead of using cause-of-death assignment by pathologists, the number of fatal tumors is estimated by a constrained nonparametric maximum likelihood estimation method. For data lacking cause-of-death information, the Peto test is modified with estimated numbers of fatal tumors and a Fleming–Harrington-type weight, which is based on an estimated tumor survival function. A bootstrap resampling method is used to estimate the weight function. The proposed testing method with the weight adjustment appears to improve the performance in various situations of single-sacrifice animal experiments. A Monte Carlo simulation study for the proposed test is conducted to assess size and power of the test. This testing approach is illustrated using a real data set.