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
Few studies focus on the application of functional data to the field of design-based survey sampling.In this paper,the scalar-onunction regression model-assisted method is proposed to estimate the finite population me...Few studies focus on the application of functional data to the field of design-based survey sampling.In this paper,the scalar-onunction regression model-assisted method is proposed to estimate the finite population means with auxiliary functional data information.The functional principal component method is used for the estimation of functional linear regression model.Our proposed functional linear regression model-assisted(FLR-assisted)estimator is asymptotically design-unbiased,consistent under mild conditions.Simulation experiments and real data analysis show that the FLR-assisted estimators are more efficient than the Horvitz-Thompson estimators under different sampling designs.展开更多
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
Approximation of finite population totals in the presence of auxiliary information is considered. A polynomial based on Lagrange polynomial is proposed. Like the local polynomial regression, Horvitz Thompson and ratio...Approximation of finite population totals in the presence of auxiliary information is considered. A polynomial based on Lagrange polynomial is proposed. Like the local polynomial regression, Horvitz Thompson and ratio estimators, this approximation technique is based on annual population total in order to fit in the best approximating polynomial within a given period of time (years) in this study. This proposed technique has shown to be unbiased under a linear polynomial. The use of real data indicated that the polynomial is efficient and can approximate properly even when the data is unevenly spaced.展开更多
受Deshpande and Prabhu Ajgaonkar(1982)设计的思想启发,本文给出了一种新的n=2时的严格πps抽样设计.当辅助变量值满足Xi/X<1/2时,提出的新设计既容易实施,又容易计算一阶和二阶包含概率.同时,可以得到Horvitz-Thompson估计量的一...受Deshpande and Prabhu Ajgaonkar(1982)设计的思想启发,本文给出了一种新的n=2时的严格πps抽样设计.当辅助变量值满足Xi/X<1/2时,提出的新设计既容易实施,又容易计算一阶和二阶包含概率.同时,可以得到Horvitz-Thompson估计量的一个非负的方差估计.通过数值比较提出设计和经典πps抽样设计,说明提出方法具有潜在应用价值.展开更多
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.
基金China Postdoctoral Science Foundation(Grant Nos.2021M691443,2021TQ0141)SUSTC Presidential Postdoctoral Fellow-ship.Huiming Zhang was supported in part by the University of Macao under UM Macao Talent Programme(UMMTP-2020-01).
文摘Few studies focus on the application of functional data to the field of design-based survey sampling.In this paper,the scalar-onunction regression model-assisted method is proposed to estimate the finite population means with auxiliary functional data information.The functional principal component method is used for the estimation of functional linear regression model.Our proposed functional linear regression model-assisted(FLR-assisted)estimator is asymptotically design-unbiased,consistent under mild conditions.Simulation experiments and real data analysis show that the FLR-assisted estimators are more efficient than the Horvitz-Thompson estimators under different sampling designs.
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Approximation of finite population totals in the presence of auxiliary information is considered. A polynomial based on Lagrange polynomial is proposed. Like the local polynomial regression, Horvitz Thompson and ratio estimators, this approximation technique is based on annual population total in order to fit in the best approximating polynomial within a given period of time (years) in this study. This proposed technique has shown to be unbiased under a linear polynomial. The use of real data indicated that the polynomial is efficient and can approximate properly even when the data is unevenly spaced.
文摘受Deshpande and Prabhu Ajgaonkar(1982)设计的思想启发,本文给出了一种新的n=2时的严格πps抽样设计.当辅助变量值满足Xi/X<1/2时,提出的新设计既容易实施,又容易计算一阶和二阶包含概率.同时,可以得到Horvitz-Thompson估计量的一个非负的方差估计.通过数值比较提出设计和经典πps抽样设计,说明提出方法具有潜在应用价值.
