Resampling Unbalanced Ranked Set Samples With Applications in Testing Hypothesis About the Population Mean

Ranked set sampling is a sampling approach that could lead to improved statistical inference when the actual measurement of the variable of interest is difficult or expensive to obtain but sampling units can be easily ordered by some means without actual quantification. In this paper, we consider the problem of bootstrapping an unbalanced ranked set sample (URSS) where the number of observations from each artificially created stratum can be unequal. We discuss resampling a URSS through transforming it into a balanced RSS and extending the existing algorithms. We propose two methods that are designed to obtain resamples from the given URSS. Algorithms are provided and several properties, including asymptotic normality of estimates, are discussed. The proposed methods are compared with the parametric bootstrap using Monte Carlo simulations for the problem of testing a hypothesis about the population mean.     

Optimal interpolation and isarithmic mapping of soil properties

The principle of optimal estimation using regionalized variable theory is extended from that of a single soil property to situations where there are two or more spatially interdependent ones. Auto and cross semi-variograms express the spatial relations among the properties concerned. They can be estimated from data and can then be used to interpolate the values of a variable by co-kriging from measurements of it plus data on one or more other properties that have been more intensively sampled. The technique of co-kriging is described and illustrated by a case study of the particle size distribution at Woburn experimental farm. There was a strong co-regionalization with common anisotropy between topsoil silt, subsoil silt and subsoil sand. This allowed topsoil silt to be estimated and mapped by co-kriging more precisely than by kriging from data on topsoil silt alone. When the auto and cross semi-variograms for a set of variables are known in advance or estimated from reconnaissance they can be used to plan an optimal sampling scheme. The main variable is sampled on a rectangular grid with finer grids for subsidiary variables. The maximum kriging variances are calculated for a range of sample spacings and relative sampling intensities. Those that match the maximum tolerable variance are potentially useful. The optimum scheme is the one that achieves the desired precision for least cost. For Woburn it is shown that measuring a main variable would need to cost at least 5 times that of a subsidiary variable to make a design for co-kriging economically sound. Such differences are unlikely for particle size fractions. Nevertheless there are many other instances in soil research where there are large differences in cost. If there is also a strong co-regionalization then savings should be possible by designing a sampling scheme that takes advantage of co-kriging.     

Ranked set sampling based on binary water quality data with covariates

A ranked set sample (RSS) is composed of independent order statistics, formed by collecting and ordering independent subsamples, then measuring only one item from each subsample. If the cost of sampling is dominated by data measurement rather than collection or ranking, the RSS technique is known to be superior to ordinary sampling. Experiments based on binary data are not designed to exploit the advantages of ranked set sampling because categorical data typical are as easily measured as ranked, making RSS methods impractical. However, in some environmental and biological studies, the success probability of a bivariate outcome is related to one or more covariates. If the covariate information is not easily quantified, but can be objectively ordered with respect to this success probability, the RSS method can be used to improve the analysis of binary data. This article considers the case in which the covariate information is modeled in terms of a mixing distribution for the success probability, and the expected success probability is of primary interest. The inference technique is demonstrated with water-quality data from the Rappahannock river in Virginia. In a general setting, the RSS estimator is shown to be superior, including cases in which error in judgment ranking is present.     

Estimating temporal change in soil monitoring: I. Statistical theory

Detecting small temporal change of spatially varying soil properties demands precise estimation. Design– and model–based methods are compared for estimating temporal change of soil properties over finite areas. Analytical expressions for the estimators and their variances arc derived for the two approaches, and formulae for the expectations of the variances under the random–process model are developed. Among the randomized designs simple, stratified, and systematic random sampling using the arithmetic mean as estimator have been studied. Pairing the sampling positions on the different occasions increases the precision of design–based estimation if the observations are positively cross–correlated. The relative precisions of the means of stratified and systematic samples depends on the spatial correlation. Neither is more precise than the other in all circumstances. The stratified design provides an unbiased estimator for the sampling error, which is not available from systematic samples. Theoretically, the geostatistical global estimator is more precise than the estimates derived from any of the classical designs when many realizations arc repeatedly sampled at random. In practice, with only a single realization of the process, this is no longer relevant. Moreover, errors in estimating the variograms add to the total error of the method. It seems that only by sampling from large auto–correlated random fields can the precisions of the methods be compared in practice.     

基于地理权重回归模型的土壤有机质空间预测

准确了解土壤有机质的空间分布是合理施肥的重要前提,也是水土流失控制及保护环境的重要基础。利用113个土壤有机质样点数据,以海拔高度、土壤侵蚀强度、土地利用、比值植被指数、样点至河流的欧氏距离、亚铁矿物指数及坡度为参考因子,来尝试利用GWR(Geographically Weighted Regression)模型探索多重因素作用下的土壤有机质空间分布,并通过与普通线性回归(ordinary least squares,OLS)相比较,来了解GWR模型的精度,进而进行了土壤有机质的空间制图,并对其制图效果进行了评价。结果表明,与OLS模型相比,GWR预测模型它能显著降低AIC(Akaike Information Criterion)值,较大程度地提高模型的决定系数,并有效地减少模型的回归残差值。从制图的总体效果看,GWR模型的预测结果与实测值的吻合程度要优于OLS模型。文章还对利用GWR模型进行回归时的样点数量、因子筛选及因子定量化等方面进行了相应的讨论。     

Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood

This article considers logistic regression analysis of binary data that are measured on a spatial lattice and repeatedly over discrete time points. We propose a spatial-temporal autologistic regression model and draw statistical inference via maximum likelihood. Due to an unknown normalizing constant in the likelihood function, we use Monte Carlo to obtain maximum likelihood estimates of the model parameters and predictive distributions at future time points. We also use path sampling to estimate the unknown normalizing constant and approximate an information criterion for model assessment. The methodology is illustrated by the analysis of a dataset of mountain pine beetle outbreaks in western Canada.     

Workweek Optimization of Experimental Designs: Exact Designs for Variable Sampling Costs

An optimal experimental design combines high-quality parameter estimation with efficient use of resources. This paper proposes a new method for heuristic optimization of experimental designs in the presence of variable sampling costs. The method finds the inexpensive designs with desirable statistical qualities and provides substantial insight regarding the relative importance (in monetary terms) of sampling at specific design points. The method is illustrated within the context of a start-stop exposure study in aquatic toxicology. Fast heuristics enable the analyses of a large range of sensitivity issues and examination of trade-offs between information and cost. The method is described for designs in which replicate sampling is prohibited; a generalization allowing for replicate sampling is provided as an appendix in the online supplemental materials.     

A Varying Coefficients Model For Estimating Finite Population Totals: A Hierarchical Bayesian Approach

In some finite sampling situations, there is a primary variable that is sampled, and there are measurements on covariates for the entire population. A Bayesian hierarchical model for estimating totals for finite populations is proposed. A nonparametric linear model is assumed to explain the relationship between the dependent variable of interest and covariates. The regression coefficients in the linear model are allowed to vary as a function of a subset of covariates nonparametrically based on B-splines. The generality of this approach makes it robust and applicable to data collected using a variety of sampling techniques, provided the sample is representative of the finite population. A simulation study is carried out to evaluate the performance of the proposed model for the estimation of the population total. Results indicate accurate estimation of population totals using the approach. The modeling approach is used to estimate the total production of avocado for a large group of groves in Mexico.     

Developing effective sampling designs for monitoring natural resources in Alaskan national parks: An example using simulations and vegetation data

Monitoring natural resources in Alaskan national parks is challenging because of their remoteness, limited accessibility, and high sampling costs. We describe an iterative, three-phased process for developing sampling designs based on our efforts to establish a vegetation monitoring program in southwest Alaska. In the first phase, we defined a sampling frame based on land ownership and specific vegetated habitats within the park boundaries and used Path Distance analysis tools to create a GIS layer that delineated portions of each park that could be feasibly accessed for ground sampling. In the second phase, we used simulations based on landcover maps to identify size and configuration of the ground sampling units (single plots or grids of plots) and to refine areas to be potentially sampled. In the third phase, we used a second set of simulations to estimate sample size and sampling frequency required to have a reasonable chance of detecting a minimum trend in vegetation cover for a specified time period and level of statistical confidence. Results of the first set of simulations indicated that a spatially balanced random sample of single plots from the most common landcover types yielded the most efficient sampling scheme. Results of the second set of simulations were compared with field data and indicated that we should be able to detect at least a 25% change in vegetation attributes over 31 years by sampling 8 or more plots per year every five years in focal landcover types. This approach would be especially useful in situations where ground sampling is restricted by access.     

Analysis of Population Change and Movement Using Robust Design Removal Data

In capture-mark-reencounter studies, Pollock’s robust design combines methods for open populations with methods for closed populations. Open population features of the robust design allow for estimation of rates of death or permanent emigration, and closed population features enhance estimation of population sizes. We describe a similar design, but for use with removal data. Data collection occurs on secondary sampling occasions clustered within primary sampling periods. Primary sampling periods are intervals of brief enough duration that it can be safely assumed that the population is unchanged by births, deaths, immigration or emigration during them; all population change and movement occurs between primary sampling periods. Our model provides a basis for inference about population size, changes in population size, and movement rates among sample locations between primary sampling periods. Movement rates are modeled as functions of distance and time. Capture probabilities are modeled as a function of effort. We apply the model to data obtained in attempting to eradicate an introduced population of veiled chameleons (Chamaeleo calyptratus) on the island of Maui in Hawaii.Supplementary materials accompanying this paper appear online.     

农作物种植面积遥感抽样调查的误差影响因素分析

空间抽样技术在农作物种植面积调查中具有不可替代的作用,各抽样要素(抽样率、抽样调查单元尺寸及布局)对于抽样精度的影响至关重要。该文以湖南省晚稻为研究对象,设计了9种抽样调查单元和31种抽样率水平,以晚稻面积百分比为分层标志进行空间分层抽样,分析抽样格网大小、抽样率及样本空间分布格局对面积估算精度的敏感性及控制途径,并建立3种影响因素对面积估算的综合评估模型。结果表明:1)作物面积估计的平均抽样误差随抽样格网尺寸的增加而增加(R2=0.92),当抽样格网控制在5 km以内时,平均误差基本限制在5%以下,标准差变幅稳定在0.12以内;2)作物面积估计的平均抽样误差随抽样率的增加而逐渐降低(R2=0.82),当抽样率达到0.4%时,平均误差基本限制在5%以内,标准差变幅稳定在0.12以内;3)在抽样率确定的情况下,样本的空间分布是影响抽样精度的重要因素,随着样本空间分布由近似均匀分布向随机分布再向集群分布变化,作物面积估计量的平均抽样误差逐渐增大,当样本空间分布的方差均值比指标0.7时,平均误差控制在5%以内,标准差变幅稳定在0.1以内;4)得到3种影响因素对面积估算精度的定量评估模型。该成果揭示了农作物种植面积抽样过程中样方尺寸、抽样率和样本空间分布对精度影响的敏感性,为农作物种植面积监测空间抽样方案的选取以及确定特定的抽样方案可以达到的面积估算水平提供了理论基础。     

Managing the Essential Zeros in Quantitative Fatty Acid Signature Analysis

Quantitative fatty acid signature analysis (QFASA) is a recent diet estimation method that depends on statistical techniques. QFASA has been used successfully to estimate the diet of predators such as seals and seabirds. Given the potential species in the predator’s diet, QFASA uses statistical methods to obtain point estimates of the proportion of each species in the diet. In this paper, inference for a population of predators is considered.     

Bayesian Inference for Animal Space Use and Other Movement Metrics

The analysis of animal movement and resource use has become a standard tool in the study of animal ecology. Telemetry devices have become quite sophisticated in terms of overall size and data collecting capacity. Statistical methods to analyze movement have responded, becoming ever more complex, often relying on state-space modeling. Estimation of movement metrics such as utilization distributions have not followed suit, relying primarily on kernel density estimation. Here we consider a method for making inference about space use that is free of all of the major problems associated with kernel density estimation of utilization distributions such as autocorrelation, irregular time gaps, and error in observed locations. Our proposed method is based on a data augmentation approach that defines use as a summary of the complete path of the animal which is only partially observed. We use a sample from the posterior distribution of the complete path to construct a posterior sample for the metric of interest. Three basic importance sampling based methods for sampling from the posterior distribution of the path are proposed and compared. We demonstrate the augmentation approach by estimating a spatial map of diving intensity for female northern fur seals in the Pribilof Islands, Alaska.     

Continuous-Time Proportional Hazards Regression for Ecological Monitoring Data

We consider a continuous-time proportional hazards model for the analysis of ecological monitoring data where subjects are monitored at discrete times and fixed sites across space. Since the exact time of event occurrence is not directly observed, we rely on dichotomous event indicators observed at monitoring times to make inference about the model parameters. We use autoregression on the response at neighboring sites from a previous time point to take into account spatial dependence. The interesting fact is utilized that the probability of observing an event at a monitoring time when the underlying hazards is proportional falls under the class of generalized linear models with binary responses and complementary log-log link functions. Thus, a maximum likelihood approach can be taken for inference and the computation can be carried out using standard statistical software packages. This approach has significant computational advantages over some of the existing methods that rely on Monte Carlo simulations. Simulation experiments are conducted and demonstrate that our method has sound finite-sample properties. A real dataset from an ecological study that monitored bark beetle colonization of red pines in Wisconsin is analyzed using the proposed models and inference. Supplementary materials that contain technical details are available online.     

Altered selection probabilities caused by avoiding the edge in field surveys

When field plots are prevented from overlapping the boundary of the tract being sampled, the selection probabilities of population elements near the edge differ from what they are when plots are permitted to overlap the boundary. In both situations, selection probabilities of edge elements differ from those of elements located far to the interior of the tract. An analytical expression and graphical depiction of the altered selection probabilities are presented, accompanied by an empirical study of the consequences on estimation for the situation where plots are pulled back from the edge until just tangent with the boundary.     

采用改进YOLOv4-Tiny模型的柑橘木虱识别

黄龙病是一种以柑橘木虱为传播媒介的毁灭性病害,其关键预防措施是在果园现场环境对柑橘木虱识别监测,辅助果农进行早期防治。该研究基于YOLOv4-Tiny模型提出一种适用于嵌入式系统的柑橘木虱识别模型。通过改进YOLOv4-Tiny模型的颈部网络,利用浅层网络的细节信息以提高模型识别柑橘木虱的平均精度;采用交叉小批量归一化（Cross mini-Batch Normalization,CmBN）方法代替批归一化（Batch Normalization,BN）方法,通过累计卷积层的输出,提升统计信息的准确度;针对柑橘木虱易被遮挡的问题,模型训练时使用Mosaic数据增强,提升模型对遮挡目标的识别能力。通过自行建立的柑橘木虱图像数据集完成模型的试验验证。结果表明,该模型的柑橘木虱平均识别精度为96.16%,在图形处理器（Graphics Processing Unit, GPU）上的推理速度为3.63 ms/帧,模型大小为24.5 MB,实现了果园环境下快速准确地识别柑橘木虱,可为黄龙病防治技术的进一步发展提供参考。     

Imputation Approaches for Animal Movement Modeling

The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online.     

Relationship between chlorophyll meter readings and nitrogen in poinsettia leaves

Chlorophyll meters are used for non-destructive estimation of leaf nitrogen (N). The objective of this study was to evaluate the readings with several chlorophyll meters (SPAD-502, atLEAF, and CCM-300), different sampling sites on leaves, number of leaves used for sampling, and different types of leaf N sampling on estimation of N in leaves of potted poinsettia (Euphorbia pulcherrima) “Prestige Red.” Results showed that all meters gave readings that were correlated for N determination and also were correlated with each other. SPAD and atLEAF showed interaction between different N treatments and different sampling sites on the leaves, while CCM readings were affected by different sampling sites on the leaf. atLEAF readings showed interaction between number of leaves sampled and different N treatments. Thus, during sensor-based leaf N estimation, sampling site on leaf, number of leaves sampled, and stage of plant development should be considered to minimize error.     

Diurnal,shade, and hybrid effects on nitrate content of young corn stalks

Abstract

Significant correlations have been found between the nitrate concentration in young corn (Zea mays L.) stalks and available soi 1 N and relative grain yield when N availability is a yield limiting factor. To successfully use nitrate tissue testing as an indicator of the N status of plants, the effects of factors other than soil or fertilizer N on the nitrate concentration in the tested tissue must be understood. Diurnal effects on stalk nitrate content were studied in a greenhouse experiment, in which stalk samples were collected every two hours for 24 hours, and in a field study, where plants in an N‐fertilizer response experiment were sampled at sunrise and at mid‐day. A significant diurnal cycle was observed for stalk nitrate content in the greenhouse, with a peak at 0800 h and a low at 1400 h, but no diurnal effect was found in the field study, possibly due to plant stress from other environmental factors. In a second greenhouse study, stalk samples from seedlings in direct and 66% shaded natural light were collected for two consecutive days and measured for nitrate content to ascertain the consequences of sampling on overcast days. Significant accumulations of nitrate occurred in stalks of plants sampled after two consecutive days of shading. Stalk samples from three standard hybrid corn trials in central Pennsylvania were measured for nitrate content to determine if hybrid differences existed. Hybrid differences in stalk nitrate were significant in only one of the three sites studied, and the variation in that one may have been due to differences in drought tolerance as well as nitrate metabolism. The results suggest that sampling strategies should be designed to minimize the effects of irradiance level on nitrate accumulation, but that differences in nitrate accumulation among corn varieties may not be a major concern in using nitrate tissue tests to predict N availability.     

Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables

Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed‐species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario‐based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd.