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.

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.

Forest inventory: further results for optimal sampling schemes based on the anticipated variance

2001 ◽  
Vol 31 (10) ◽  
pp. 1845-1853 ◽  
Author(s):  
Daniel Mandallaz ◽  
Adrian Lanz
Keyword(s):  
Forest Inventory ◽  
Optimal Allocation ◽  
Optimality Criterion ◽  
Second Phase ◽  
Optimal Sampling ◽  
Two Stage ◽  
Two Phase ◽  
Sampling Density ◽  
Sampling Schemes ◽  
Inclusion Probabilities

This work presents optimal allocation rules for two-phase, two-stage sampling schemes in which the sampling density and the costs of the second phase can vary over domains. The optimality criterion is based on the anticipated variance. It also gives an improved version of discrete approximation for the resulting inclusion probabilities. An example illustrates 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.

Forest inventory with optimal two-phase two-stage sampling schemes based on the anticipated variance

1999 ◽  
Vol 29 (11) ◽  
pp. 1691-1708 ◽  
Author(s):  
Daniel Mandallaz ◽  
Ronghua Ye
Keyword(s):  
Spatial Distribution ◽  
Forest Inventory ◽  
Poisson Model ◽  
Optimal Sampling ◽  
Two Stage ◽  
Two Phase ◽  
Sampling Schemes ◽  
One Stage ◽  
Sampling Procedures

This work presents optimal sampling schemes for forest inventory. The sampling procedures are optimal in the sense that they minimize the anticipated variance for given costs or conversely, the anticipated variance is the average of the design-based variance under a local Poisson model for the spatial distribution of the trees. The resulting optimal inclusion rules are either probability proportional to size, in one-stage procedures, or a combination of probability proportional to prediction and probability proportional to error, in two-stage procedures. Best feasible approximations of the exact optimal sampling schemes are also given.

scholarly journals Socio demography of mental retardation: A community-based study from a goitre zone in rural sub-Himalayan India

2015 ◽  
Vol 6 (02) ◽  
pp. 165-169 ◽  
Author(s):  
Shailja Sharma ◽  
Sunil Kumar Raina ◽  
Ashok Kumar Bhardwaj ◽  
Sanjeev Chaudhary ◽  
Vipasha Kashyap ◽  
...  
Keyword(s):  
Mental Retardation ◽  
Middle Class ◽  
Economic Status ◽  
Local Population ◽  
Sampling Technique ◽  
Himachal Pradesh ◽  
Cluster Sampling ◽  
Second Phase ◽  
Two Phase ◽  
Cross Sectional

ABSTRACT Introduction: Mental retardation is one of the most common disabilities of childhood which can be prevented by timely identification of the causative agent and an adequate management accordingly. District Kangra lies in the sub-Himalayan belt and forms a part of the 2400 km long goitre belt along the southern slopes of the Himalayas. Objective: To study the prevalence of mental retardation among children (1-10) years of age. Materials and Methods: A two-phase cross-sectional study was conducted in the rural area of district Kangra. A 30-cluster sampling technique was used to screen a population of children 1-10 years of age from five randomly selected panchayats (village government units) of district Kangra. The screening was based on a modified version of the ten questions screen, adapted to the local population. In the first phase a door to door survey was done to identify suspects of mental retardation. In the second phase, the children found positive in the first phase were called for examination by the pediatrician to confirm mental retardation. Results: A total of 2420 children were screened in the first phase of which 95 tested positive. About 52 of these children were found to be mentally retarded in the second phase giving a prevalence of 2.15%. The 69% of these children belonged to the lower middle class and 28.3% belonged to middle class families using the Uday Parekh scale for assessment of the socio-economic status. Conclusion: Prevalence of mental retardation is high in district Kangra of Himachal Pradesh in comparison to other states of India. This could be attributed to the good primary health care in Himachal Pradesh where institutional deliveries are about 70%. This may have led to better survival of children with congenital disorders and those that suffer perinatal trauma.

scholarly journals Malnutrition as a cause of mental retardation: A population-based study from Sub-Himalayan India

2016 ◽  
Vol 07 (03) ◽  
pp. 341-345 ◽  
Author(s):  
Sunil Kumar Raina ◽  
Shailja Sharma ◽  
Ashok Bhardwaj ◽  
Mitasha Singh ◽  
Sanjeev Chaudhary ◽  
...  
Keyword(s):  
Mental Retardation ◽  
Nutritional Assessment ◽  
Local Population ◽  
Sampling Technique ◽  
Positive Association ◽  
North India ◽  
Cluster Sampling ◽  
Second Phase ◽  
Two Phase ◽  
Cross Sectional

ABSTRACT Background: Mental retardation is one of the most common disabilities of childhood. The research on childhood malnutrition and its relationship with cognitive functioning suggests that malnutrition alone does not cause mental retardation. Objective: To identify the relation between malnutrition and cognition among children from a Sub-Himalayan state in North India. Materials and Methods: A two-phase cross-sectional study was conducted in the rural, urban, and slum area of district Kangra. A 30-cluster sampling technique was used to screen a population of children 1–10 years of age from five randomly selected panchayats (village government units) of district Kangra. The screening was based on a modified version of the ten questions screen, adapted to the local population. In the first phase, a door-to-door survey was done to identify suspects of mental retardation. In the second phase, the children found positive in the first phase were called for clinical examination to confirm mental retardation. Anthropometric assessment of all study children was done by measuring weight and height. The nutritional assessment was done by categorizing them according to Waterlow classification for malnutrition. Results: Out of the total 5300 children, 1.7% were diagnosed as mentally retarded. No positive association was reported with different types of malnutrition and mental retardation. A weakly positive association existed between nutritional status and mental retardation (correlation coefficient-0.04). Children who were both wasted and stunted had the highest risk (odds ratio, 95% confidence interval - 5.57, 2.29–10.36) of mental retardation as compared to normal. Conclusion: Malnutrition may be one of the causes but certainly not the only cause of mental retardation. Other causes may be contributing more significantly toward it.

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.

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