k-step adaptive cluster sampling with Horvitz–Thompson estimator

2018 ◽  
Vol 11 (02) ◽  
pp. 1850029
Author(s):  
Guangyu Zhu ◽  
Liyong Fu
Keyword(s):  
Sample Size ◽  
Design Method ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Sample Mean ◽  
Final Sample ◽  
One Step ◽  
Mean And Variance ◽  
Variance Estimate ◽  
Variance Estimates

Adaptive cluster sampling (ACS) has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a [Formula: see text]-step ACS based on Horvitz–Thompson (HT) estimator was developed and an unbiased estimator was derived. The [Formula: see text]-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen–Hurwitz (ACS-HH) and ACS-HT estimators, and [Formula: see text]-step ACS-HT estimator. The effectiveness of using different [Formula: see text]-step sizes was also compared. The results showed that [Formula: see text]-step ACS-HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species. [Formula: see text]-step ACS without replacement was slightly more effective than that with replacement. In [Formula: see text]-step ACS, the variance estimate of one-step ACS is much larger than other [Formula: see text]-step ACS ([Formula: see text]), but it is smaller than ACS. This implies that [Formula: see text]-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.

scholarly journals Efficiency of Adaptive Cluster Sampling for Estimating Plant Disease Incidence

2010 ◽  
Vol 100 (7) ◽  
pp. 663-670 ◽  
Author(s):  
P. S. Ojiambo ◽  
H. Scherm
Keyword(s):  
Sample Size ◽  
Random Sampling ◽  
Disease Incidence ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Initial Sample ◽  
Animal Populations ◽  
Final Sample ◽  
Minimum Number

Conventional sampling designs such as simple random sampling (SRS) tend to be inefficient when assessing rare and highly clustered populations because most of the time is spent evaluating empty quadrats, leading to high error variances and high cost. In previous studies with rare plant and animal populations, adaptive cluster sampling, where sampling occurs preferentially in the neighborhood of quadrats in which the species of interest is detected during the sampling bout, has been shown to estimate population parameters with greater precision at an effort comparable to SRS. Here, we use computer simulations to evaluate the efficiency of adaptive cluster sampling for estimating low levels of disease incidence (0.1, 0.5, 1.0, and 5.0%) at various levels of aggregation of infected plants having variance-to-mean ratios (V/M) of ≈1, 3, 5, and 10. For each simulation, an initial sample size of 50, 100, and 150 quadrats was evaluated, and the condition to adapt neighborhood sampling (CA), i.e., the minimum number of infected plants per quadrat that triggers a switch from random sampling to sampling in neighboring quadrats, was varied from 1 to 4 (corresponding to 7.7 to 30.8% incidence of infected plants per quadrat). The simulations showed that cluster sampling was consistently more precise than SRS at a field-level disease incidence of 0.1 and 0.5%, especially when diseased plants were highly aggregated (V/M = 5 or 10) and when the most liberal condition to adapt (CA = 1) was used. One drawback of adaptive cluster sampling is that the final sample size is unknown at the beginning of the sampling bout because it depends on how often neighborhood sampling is triggered. In our simulations, the final sample size was close to the initial sample size for disease incidence up to 1.0%, especially when a more conservative condition to adapt (CA > 1) was used. For these conditions, the effect of disease aggregation was minor. In summary, both precision and the sample size required with adaptive cluster sampling responded similarly to disease incidence and aggregation, i.e., both were most favorable at the lowest disease incidence with the highest levels of clustering. However, whereas relative precision was optimized with the most liberal condition to adapt, the ratio of final to initial sample size was best for more conservative CA values, indicating a tradeoff. In our simulations, precision and final sample size were both simultaneously favorable for disease incidence of up to 1.0%, but only when infected plants were most aggregated (V/M = 10).

scholarly journals Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255256
Author(s):  
Mohammad Salehi ◽  
David R. Smith
Keyword(s):  
Binary Data ◽  
Sampling Design ◽  
A Priori ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Inverse Sampling ◽  
Two Stage ◽  
Population Total ◽  
Unbiased Estimators ◽  
Final Sample

Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

scholarly journals Adaptive cluster sampling based on ranked sets

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Girish Chandra ◽  
Neeraj Tiwari ◽  
Hukum Chandra
Keyword(s):  
Hot Spot ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Rare And Endangered Species ◽  
Rare And Endangered ◽  
Mean And Variance ◽  
Unequal Allocation ◽  
Pollution Concentrations ◽  
Better Than

In many surveys, characteristic of interest is sparsely distributed but highly aggregated; in such situations the adaptive cluster sampling is very useful. Examples of such populations can be found in fisheries, mineral investigations (unevenly distributed ore concentrations), animal and plant populations (rare and endangered species), pollution concentrations and hot spot investigations, and epidemiology of rare diseases. Ranked Set Sampling (RSS) is another useful technique for improving the estimates of mean and variance when the sampling units in a study can be more easily ranked than measured. Under equal and unequal allocation, RSS is found to be more precise than simple random sampling, as it contains information about each order statistics. This paper deal with the problem in which the value of the characteristic under study on the sampled places is low or negligible but the neighbourhoods of these places may have a few scattered pockets of the same. We proposed an adaptive cluster sampling theory based on ranked sets. Different estimators of the population mean are considered and the proposed design is demonstrated with the help of one simple example of small populations. The proposed procedure appears to perform better than the existing procedures of adaptive cluster sampling.

scholarly journals Intention to Comply With Mandatory Hurricane Evacuation Orders Among Persons Living Along a Coastal Area

2013 ◽  
Vol 7 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Belinda M. Reininger ◽  
Sartaj Alam Raja ◽  
Ana Sanchez Carrasco ◽  
Zhongxue Chen ◽  
Barbara Adams ◽  
...  
Keyword(s):  
Public Health ◽  
Coastal Area ◽  
Sampling Strategy ◽  
Coastal Areas ◽  
Cluster Sampling ◽  
Level Variation ◽  
Hurricane Evacuation ◽  
Public Health Preparedness ◽  
Census Tracts ◽  
Final Sample

AbstractObjectivesWe examined the intention to comply with mandatory hurricane evacuation orders among respondents living in coastal areas with pronounced poverty by demographic and location characteristics.MethodsA 3-county door-to-door survey was conducted with 1 randomly selected resident per household. Households were selected using a 2-stage cluster sampling strategy and stratified by county. The final sample included 3088 households in 100 census tracts across 3 counties.ResultsFindings suggest that the majority of residents living in areas prone to hurricanes intend to comply with mandatory evacuation orders regardless of income level. Variation in intention to comply with mandatory evacuation orders is shown by age, gender, ethnicity, education, acculturation, county, and distance from shoreline.ConclusionsThe demonstrated high intention to comply with evacuation orders in impoverished areas suggests a need for improved planning to evacuate the most vulnerable residents. Demographic and location characteristics associated with decreased intention to comply may be considered for targeting messages and education before disasters to modifying intentions and plans to evacuate. (Disaster Med Public Health Preparedness. 2013;7:46-54)

Estimation of rare and clustered population variance in adaptive cluster sampling

2018 ◽  
Vol 48 (21) ◽  
pp. 5387-5400
Author(s):  
Muhammad Nouman Qureshi ◽  
Sadia Khalil ◽  
Chang-Tai Chao ◽  
Muhammad Hanif
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Population Variance

scholarly journals Stable Model Predictive Control Design: Sequential Approach

2011 ◽  
Vol 62 (2) ◽  
pp. 99-103
Author(s):  
Vojtech Veselý
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Design Method ◽  
Control Design ◽  
Sequential Design ◽  
Stable Model ◽  
Sequential Approach ◽  
Prediction Horizon ◽  
Guaranteed Cost ◽  
One Step

Stable Model Predictive Control Design: Sequential Approach The paper addresses the problem of output feedback stable model predictive control design with guaranteed cost. The proposed design method pursues the idea of sequential design for N prediction horizon using one-step ahead model predictive control design approach. Numerical examples are given to illustrate the effectiveness of the proposed method.

Stratified adaptive cluster sampling

Biometrika ◽  
1991 ◽  
Vol 78 (2) ◽  
pp. 389-397 ◽  
Author(s):  
STEVEN K. THOMPSON
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling

scholarly journals An N-Pact Factor for Clinical Psychological Research

2018 ◽  
Author(s):  
Kathleen Wade Reardon ◽  
Avante J Smack ◽  
Kathrin Herzhoff ◽  
Jennifer L Tackett
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Empirical Studies ◽  
Psychological Research ◽  
Medium Effect ◽  
Medium Effect Size ◽  
One Step ◽  
Hard To Reach Populations ◽  
Self Examination ◽  
Adequate Sample Size

Although an emphasis on adequate sample size and statistical power has a long history in clinical psychological science (Cohen, 1992), increased attention to the replicability of scientific findings has again turned attention to the importance of statistical power (Bakker, van Dijk, & Wicherts, 2012). These recent efforts have not yet circled back to modern clinical psychological research, despite the continued importance of sample size and power in producing a credible body of evidence. As one step in this process of scientific self-examination, the present study estimated an N-pact Factor (the statistical power of published empirical studies to detect typical effect sizes; Fraley & Vazire, 2014) in two leading clinical journals (the Journal of Abnormal Psychology; JAP, and the Journal of Consulting and Clinical Psychology; JCCP) for the years 2000, 2005, 2010, and 2015. Study sample size, as one proxy for statistical power, is a useful focus because it allows direct comparisons with other subfields and may highlight some of the core methodological differences between clinical and other areas (e.g., hard-to-reach populations, greater emphasis on correlational designs). We found that, across all years examined, the average median sample size in clinical research is 179 participants (175 for JAP and 182 for JCCP). The power to detect a small-medium effect size of .20 is just below 80% for both journals. Although the clinical N-pact factor was higher than that estimated for social psychology, the statistical power in clinical journals is still limited to detect many effects of interest to clinical psychologists, with little evidence of improvement in sample sizes over time.

scholarly journals Role of Cooperative Societies in Boosting Agricultural Production & Entrepreneurship in the Kebbi State of Nigeria.

2017 ◽  
pp. 234-351
Author(s):  
Kamelshewer Lohana Et al.,
Keyword(s):  
Local Government ◽  
Sample Size ◽  
Agricultural Production ◽  
Null Hypothesis ◽  
Food Production ◽  
Sampling Technique ◽  
Added Value ◽  
Cluster Sampling ◽  
Survey Results

The study Assess the Role & contributions of cooperative societies in boosting agricultural production & Entrepreneurship in the Kebbi State of Nigeria. A total of 120 sample size was used for the study. Cluster sampling technique was used to obtaining information from sample respondents (members of farmers’ cooperative societies). Sixty (60) questionnaires were administered to sixty respondents, each in both Zuru and Yauri Local Government Areas. Data collected was analysed and interpreted using simple percentage and descriptive methods. The major conclusions drawn from this research were: survey results, regarding effectiveness of cooperative societies in improving agricultural production & Entrepreneurship, have shown that 33.3% and 25% of the respondents in Zuru and Yauri Local Government Areas reported promoting farmers’ participation in agriculture, while 25% and 46% agreed to boost agricultural production in the study areas. About 36.6% and 35% believed in the effectiveness of cooperative societies in increasing food production. Sample respondents in the two Local Government Areas 5% and 3.3% reported all of the above indicators increase the effectiveness of cooperatives to agriculture. Survey results regarding the role of cooperatives in boosting Entrepreneurship in the study areas shows that 75% Zuru 88.3% Yauri agreed that cooperatives have added value to boosting Agric production & Entrepreneurship and only 15% and 11.6% did not agree with the above opinion. Many problems were identified that affects the smooth functioning of cooperatives and solutions for addressing the problems were recommended. Therefore it was concluded that Null Hypothesis HO is rejected and Alternate Hypothesis HA is accepted.

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