Adaptive cluster sampling (ACS) has been a very important tool in estimation of population parameters of rare and clustered population. The fundamental idea behind this sampling plan is to decide on an initial sample ...Adaptive cluster sampling (ACS) has been a very important tool in estimation of population parameters of rare and clustered population. The fundamental idea behind this sampling plan is to decide on an initial sample from a defined population and to keep on sampling within the vicinity of the units that satisfy the condition that at least one characteristic of interest exists in a unit selected in the initial sample. Despite being an important tool for sampling rare and clustered population, adaptive cluster sampling design is unable to control the final sample size when no prior knowledge of the population is available. Thus adaptive cluster sampling with data-driven stopping rule (ACS’) was proposed to control the final sample size when prior knowledge of population structure is not available. This study examined the behavior of the HT, and HH estimator under the ACS design and ACS’ design using artificial population that is designed to have all the characteristics of a rare and clustered population. The efficiencies of the HT and HH estimator were used to determine the most efficient design in estimation of population mean in rare and clustered population. Results of both the simulated data and the real data show that the adaptive cluster sampling with stopping rule is more efficient for estimation of rare and clustered population than ordinary adaptive cluster sampling.展开更多
Two-stage adaptive cluster sampling and two-stage conventional sampling designs were used to estimate population total of Fringe-Eared Oryx that are clustered and sparsely distributed. The study region was Amboseli-We...Two-stage adaptive cluster sampling and two-stage conventional sampling designs were used to estimate population total of Fringe-Eared Oryx that are clustered and sparsely distributed. The study region was Amboseli-West Kilimanjaro and Magadi-Natron cross boarder landscape between Kenya and Tanzania. The study region was partitioned into different primary sampling units with different secondary sampling units that were of different sizes. Results show that two-stage adaptive cluster sampling design is efficient compared to simple random sampling and the conventional two- stage sampling design. The design is less variable compared to the conventional two-stage sampling design.展开更多
An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have neve...An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have never been exploited for establishment surveys. However, the rapidly increasing availability of geo- referenced information about business units makes that possible. In business studies, it may indeed be important to take into account the presence of spatial autocorrelation or spatial trends in the variables of interest, in order to have more precise and efficient estimates. The opportunity of using the most innovative spatial sampling designs in business surveys, in order to produce samples that are well spread in space, is here tested by means of Monte Carlo experiments. For all designs, the Horvitz-Thompson estimator of the population total is used both with equal and unequal inclusion probabilities. The efficiency of sampling designs is evaluated in terms of relative RMSE and efficiency gain compared with designs ignoring the spatial information. Furthermore, an evaluation of spatially balancing samples is also conducted.展开更多
针对无法直接获取训练样本的遥感影像分类问题,从满足条件的其他影像中选择替代训练样本是最直接的方法,但由于地物类型在不同影像中的辐射环境不同,导致替代训练样本对待分类影像的代表性较差,无法保证分类精度。以直推式支持向量机(tr...针对无法直接获取训练样本的遥感影像分类问题,从满足条件的其他影像中选择替代训练样本是最直接的方法,但由于地物类型在不同影像中的辐射环境不同,导致替代训练样本对待分类影像的代表性较差,无法保证分类精度。以直推式支持向量机(transductive support vector machine,TSVM)分类为例,发展了一种基于半监督学习的遥感影像训练样本时空拓展方法。该方法采用非监督方法从待分类影像中选择大量未标记样本,挖掘各类地物在特征空间中的结构信息;以替代训练样本所拟合的分类面为初始面,通过自适应渐进式的优化,实现对待分类影像的高精度分类。该方法要求训练样本的来源影像与待分类影像具有相似的地物分布和相近的时相。以SPOT5和QuickBird影像分类为例,分别通过基于像元的和基于分割对象的分类实验证实,该文提出的方法可有效地实现训练样本的时空拓展应用。展开更多
文摘Adaptive cluster sampling (ACS) has been a very important tool in estimation of population parameters of rare and clustered population. The fundamental idea behind this sampling plan is to decide on an initial sample from a defined population and to keep on sampling within the vicinity of the units that satisfy the condition that at least one characteristic of interest exists in a unit selected in the initial sample. Despite being an important tool for sampling rare and clustered population, adaptive cluster sampling design is unable to control the final sample size when no prior knowledge of the population is available. Thus adaptive cluster sampling with data-driven stopping rule (ACS’) was proposed to control the final sample size when prior knowledge of population structure is not available. This study examined the behavior of the HT, and HH estimator under the ACS design and ACS’ design using artificial population that is designed to have all the characteristics of a rare and clustered population. The efficiencies of the HT and HH estimator were used to determine the most efficient design in estimation of population mean in rare and clustered population. Results of both the simulated data and the real data show that the adaptive cluster sampling with stopping rule is more efficient for estimation of rare and clustered population than ordinary adaptive cluster sampling.
文摘Two-stage adaptive cluster sampling and two-stage conventional sampling designs were used to estimate population total of Fringe-Eared Oryx that are clustered and sparsely distributed. The study region was Amboseli-West Kilimanjaro and Magadi-Natron cross boarder landscape between Kenya and Tanzania. The study region was partitioned into different primary sampling units with different secondary sampling units that were of different sizes. Results show that two-stage adaptive cluster sampling design is efficient compared to simple random sampling and the conventional two- stage sampling design. The design is less variable compared to the conventional two-stage sampling design.
文摘An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have never been exploited for establishment surveys. However, the rapidly increasing availability of geo- referenced information about business units makes that possible. In business studies, it may indeed be important to take into account the presence of spatial autocorrelation or spatial trends in the variables of interest, in order to have more precise and efficient estimates. The opportunity of using the most innovative spatial sampling designs in business surveys, in order to produce samples that are well spread in space, is here tested by means of Monte Carlo experiments. For all designs, the Horvitz-Thompson estimator of the population total is used both with equal and unequal inclusion probabilities. The efficiency of sampling designs is evaluated in terms of relative RMSE and efficiency gain compared with designs ignoring the spatial information. Furthermore, an evaluation of spatially balancing samples is also conducted.
文摘针对无法直接获取训练样本的遥感影像分类问题,从满足条件的其他影像中选择替代训练样本是最直接的方法,但由于地物类型在不同影像中的辐射环境不同,导致替代训练样本对待分类影像的代表性较差,无法保证分类精度。以直推式支持向量机(transductive support vector machine,TSVM)分类为例,发展了一种基于半监督学习的遥感影像训练样本时空拓展方法。该方法采用非监督方法从待分类影像中选择大量未标记样本,挖掘各类地物在特征空间中的结构信息;以替代训练样本所拟合的分类面为初始面,通过自适应渐进式的优化,实现对待分类影像的高精度分类。该方法要求训练样本的来源影像与待分类影像具有相似的地物分布和相近的时相。以SPOT5和QuickBird影像分类为例,分别通过基于像元的和基于分割对象的分类实验证实,该文提出的方法可有效地实现训练样本的时空拓展应用。
