New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

2013 ◽  
Vol 43 (11) ◽  
pp. 1023-1031 ◽  
Author(s):  
Daniel Mandallaz ◽  
Jochen Breschan ◽  
Andreas Hill
Keyword(s):  
Auxiliary Information ◽  
Cluster Sampling ◽  
Lidar Data ◽  
Second Phase ◽  
Regression Estimator ◽  
Two Phase ◽  
Sampling Schemes ◽  
Forest Inventories ◽  
Phase Sampling ◽  
Two Phase Sampling

We consider two-phase sampling schemes where one component of the auxiliary information is known in every point (“wall-to-wall”) and a second component is available only in the large sample of the first phase, whereas the second phase yields a subsample with the terrestrial inventory. This setup is of growing interest in forest inventory thanks to the recent advances in remote sensing, in particular, the availability of LiDAR data. We propose a new two-phase regression estimator for global and local estimation and derive its asymptotic design-based variance. The new estimator performs better than the classical regression estimator. Furthermore, it can be generalized to cluster sampling and two-stage tree sampling within plots. Simulations and a case study with LiDAR data illustrate the theory.

scholarly journals Optimal sampling schemes based on the anticipated variance with lack of fit

2002 ◽  
Vol 32 (12) ◽  
pp. 2236-2243 ◽  
Author(s):  
D Mandallaz
Keyword(s):  
Estimation Procedure ◽  
Cluster Sampling ◽  
Second Phase ◽  
Optimal Sampling ◽  
Two Phase ◽  
Sampling Schemes ◽  
Lack Of Fit ◽  
Important Improvement ◽  
Phase Sampling ◽  
Simple Stochastic Model

This note presents an important improvement for optimal sampling schemes based on the anticipated variance. The anticipated variance is defined as the average of the design-based variance under a simple stochastic model in which the trees are assumed to be uniformly and independently distributed within a given number of so-called Poisson strata. We consider two-phase two-stage cluster sampling schemes in which costs and terrestrial second-phase sampling density can vary over domains. The estimation procedure is based on post-stratification with respect to so-called working strata that do not need to be identical with the Poisson strata, usually unknown, which induces a lack of fit. It is then possible to derive analytically the optimal sampling schemes. Data from the Swiss National Inventory illustrates the method.

scholarly journals Design-based properties of some small-area estimators in forest inventory with two-phase sampling

2013 ◽  
Vol 43 (5) ◽  
pp. 441-449 ◽  
Author(s):  
Daniel Mandallaz
Keyword(s):  
Small Area ◽  
Regression Coefficients ◽  
Auxiliary Variables ◽  
Two Phase ◽  
Sampling Schemes ◽  
Forest Inventories ◽  
Phase Sampling ◽  
Two Phase Sampling ◽  
Asymptotic Variances

We consider the small-area estimation problem for forest inventories with two-phase sampling schemes. We propose an improvement to the synthetic estimator, when the true mean of the auxiliary variables over the small area is unknown and must be estimated, and likewise to the residual corrected small-area estimator. We derive the asymptotic design-based variances of these new estimators, the pseudo-synthetic and pseudo-small-area estimators, by also incorporating the design-based variance of the regression coefficients. We then propose a very simple mathematical device that transforms pseudo-small-area estimators into pseudo-synthetic estimators, which is very convenient for deriving asymptotic variances. The results are extended to cluster and two-stage sampling at the plot level. A case study and a simulation illustrate the theory.

scholarly journals A combination sampling approach for epidemiologic research in humanitarian settings: a case analysis of a study of depressive disorder prevalence among refugees in Greece

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Danielle N. Poole ◽  
Nathaniel A. Raymond ◽  
Jos Berens ◽  
Mark Latonero ◽  
Julie Ricard ◽  
...  
Keyword(s):  
Depressive Disorder ◽  
Case Analysis ◽  
Refugee Camp ◽  
Second Phase ◽  
Two Phase ◽  
Humanitarian Emergencies ◽  
Prevention And Treatment ◽  
Phase Sampling ◽  
Two Phase Sampling ◽  
Sampling Approach

Abstract Background Understanding the burden of common mental health disorders, such as depressive disorder, is the first step in strengthening prevention and treatment in humanitarian emergencies. However, simple random sampling methods may lead to a high risk of coercion in settings characterized by a lack of distinction between researchers and aid organizations, mistrust, privacy concerns, and the overarching power differential between researchers and populations affected by crises. This case analysis describes a sampling approach developed for a survey study of depressive disorder in a Syrian refugee camp in Greece (n = 135). Discussion Syrian refugees face an extraordinarily high burden of depressive disorder during the asylum process (43%), necessitating population screening, prevention, and treatment. In order to preserve the informed consent process in this refugee camp setting, the research team developed a two-phase sampling strategy using a map depicting the geographical layout of the housing units within the camp. In the first phase, camp management announced a research study was being undertaken and individuals were invited to volunteer to participate. The participants’ container (housing) numbers were recorded on the map, but were not linked to the survey data. Then, in the second phase, the camp map was used for complementary sampling to reach a sample sufficient for statistical analysis. As a result of the two phases of the sampling exercise, all eligible adults from half the containers in each block were recruited, producing a systematic, age- and sex-representative sample. Conclusions Combining sampling procedures in humanitarian emergencies can reduce the risk of coerced consent and bias by allowing participants to approach researchers in the first phase, with a second phase of sampling conducted to recruit a systematic sample. This case analysis illuminates the feasibility of a two-phase sampling approach for drawing a quasi-random, representative sample in a refugee camp setting.

scholarly journals A Generalized Two Phase Sampling Estimator of Ratio of Population Means Using Auxiliary Information

2021 ◽  
Author(s):  
Tarunpreet Kaur Ahuja ◽  
Peeyush Misra ◽  
O. K. Belwal
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Two Phase ◽  
Population Means ◽  
Minimum Mean Squared Error ◽  
Second Moments ◽  
Squared Error ◽  
Phase Sampling ◽  
Theoretical Results ◽  
Two Phase Sampling

This paper addresses the problem of estimating ratio of two population means by using quantitative auxiliary knowledge in the form of first and second moments. Through this paper, an improved generalized two phase sampling estimator has been proposed. The relative bias and mean squared error of the suggested estimator has been derived and studied. Also, a comparative study with the conventional estimators has been included to establish its superiority. Besides theoretical comparisons, a subset of optimum estimators having the same minimum mean squared error (MSE) is also explored. An empirical study is also carried out to support theoretical results.

Estimation of Variance of Regression Estimator in two Phase Sampling

2000 ◽  
Vol 50 (1-2) ◽  
pp. 49-64 ◽  
Author(s):  
Sarjinder Singh
Keyword(s):  
Higher Order ◽  
Subject Classification ◽  
Regression Estimator ◽  
Secondary 62F10 ◽  
Two Phase ◽  
Primary 62D05 ◽  
Phase Sampling ◽  
Estimation Of Variance ◽  
Two Phase Sampling ◽  
Calibration Approach

In the present investigation, an improved strategy in two phase sampling for estimating the variance of the regression estimator has been developed. The proposed technique has been found to be more efficient than the strategy adopted by Hidiroglou and Sarndal (1995, 1998). The proposed strategy here is analogous to the higher order calibration approach proposed by Singh, Horn and Yu (1998). Many new estimators for estimating the variance of regression estimator have been studied theoretically as well as empirically. AMS (2000) Subject Classification: Primary 62D05; Secondary 62F10.

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