Еmpirical method for estimation of the optimum size of random point samples for assessment areas of land cover from space images

A promising fast method for estimating land cover areas from satellite imagery is the use of random point sampling. This method allows you to obtain area values without spatially continuous mapping of land areas. The accuracy of the area estimate by this method depends on the sample size. The presented work describes a method for empirically finding the optimal sample size. To use this method, you must select a key site for which a reference land cover exists. For the key site, we perform multiple generation of samples of different sizes. Further, using these samples, we estimate the area of land cover. Comparison of the obtained areas with the reference areas allows you to calculate the measurement error. Analysis of the mean and the range of errors for different sample sizes allows us to identify the moment when the error ceases to decrease significantly with an increase in the sample size. This sample size is optimal. We tested the proposed method on the example of the Kalach Upland. The size range from 100 to 3000 sampling points per key site is analyzed (the size of the sampling in the row increases by 100 points). For each element of this row, we created 1000 samples of the corresponding size. We then analyzed the effect of sample size on the overall relative error in area estimates. The analysis showed that for the investigated key site the optimal sample size is 1000 points (1.1 points/km2). With this sample size, the overall relative error in determining areas was 4.0 % on average, and the maximum error was 9.9 %. Similar accuracy should be at the same sample size for other uplands in the foreststeppe and steppe zones of the East European plain.

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Using the potential outcome framework to estimate optimal sample size for cluster randomized trials: a simulation-based algorithm

Journal of Statistical Computation and Simulation ◽

10.1080/00949655.2021.1946806 ◽

2021 ◽

pp. 1-27

Author(s):

Ruoshui Zhai ◽

Roee Gutman

Keyword(s):

Sample Size ◽

Randomized Trials ◽

Cluster Randomized Trials ◽

Optimal Sample Size ◽

Potential Outcome ◽

Optimal Sample ◽

Outcome Framework ◽

Simulation Based ◽

Cluster Randomized

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Adjustment of the Analytical Data of Chemical Constituents of Tea Leaf by its Maturity and Optimal Sample Size

Nippon Nōgeikagaku Kaishi ◽

10.1271/nogeikagaku1924.30.31 ◽

1956 ◽

Vol 30 (1) ◽

pp. 31-34

Author(s):

Hideichi TORII ◽

Isao OTA ◽

Jun KANAZAWA

Keyword(s):

Sample Size ◽

Chemical Constituents ◽

Analytical Data ◽

Optimal Sample Size ◽

Optimal Sample ◽

Tea Leaf

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A Simple Note on the Determination of the Optimal Sample Size in χ2-goodness of Fit-test (Comparison of Observed Frequencies with Theoretically Expected Frequencies)

Biometrical Journal ◽

10.1002/bimj.4710200611 ◽

1978 ◽

Vol 20 (6) ◽

pp. 623-624 ◽

Cited By ~ 1

Author(s):

M. H. Hühn

Keyword(s):

Sample Size ◽

Goodness Of Fit ◽

Goodness Of Fit Test ◽

Optimal Sample Size ◽

Optimal Sample

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An Adaptive Algorithm for the Optimal Sample Size in the Non-Stationary Data-Driven Newsvendor Problem

Operations Research/Computer Science Interfaces Series - Extending the Horizons: Advances in Computing, Optimization, and Decision Technologies ◽

10.1007/978-0-387-48793-9_6 ◽

2007 ◽

pp. 77-96 ◽

Cited By ~ 2

Author(s):

Gokhan Metan ◽

Aurélie Thiele

Keyword(s):

Sample Size ◽

Adaptive Algorithm ◽

Newsvendor Problem ◽

Data Driven ◽

Optimal Sample Size ◽

Optimal Sample

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Effectiveness and policies analysis of pool testing method for COVID-19

Kybernetes ◽

10.1108/k-01-2021-0052 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yang Liu ◽

Yi Chen ◽

Kefan Xie ◽

Jia Liu

Keyword(s):

Sample Size ◽

Infection Rate ◽

Large Population ◽

Optimal Sample Size ◽

Content Type ◽

Testing Method ◽

Multiple Sampling ◽

Optimal Sample ◽

Test Result ◽

Sampling Approach

PurposeThis research aims to figure out whether the pool testing method of SARS-CoV-2 for COVID-19 is effective and the optimal sample size is in one bunch. Additionally, since the infection rate was unknown at the beginning, this research aims to propose a multiple sampling approach that enables the pool testing method to be utilized successfully.Design/methodology/approachThe authors verify that the pool testing method of SARS-CoV-2 for COVID-19 is effective under the situation of the shortage of nucleic acid detection kits based on probabilistic modeling. In this method, the testing is performed on several samples of the cases together as a bunch. If the test result of the bunch is negative, then it is shown that none of the cases in the bunch has been infected with the novel coronavirus. On the contrary, if the test result of the bunch is positive, then the samples are tested one by one to confirm which cases are infected.FindingsIf the infection rate is extremely low, while the same number of detection kits is used, the expected number of cases that can be tested by the pool testing method is far more than that by the one-by-one testing method. The pool testing method is effective only when the infection rate is less than 0.3078. The higher the infection rate, the smaller the optimal sample size in one bunch. If N samples are tested by the pool testing method, while the sample size in one bunch is G, the number of detection kits required is in the interval (N/G, N).Originality/valueThis research proves that the pool testing method is not only suitable for the situation of the shortage of detection kits but also the situation of the overall or sampling detection for a large population. More importantly, it calculates the optimal sample size in one bunch corresponding to different infection rates. Additionally, a multiple sampling approach is proposed. In this approach, the whole testing process is divided into several rounds in which the sample sizes in one bunch are different. The actual infection rate is estimated gradually precisely by sampling inspection in each round.

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Estimation of optimum sample size allocation: An illustration with body mass index for evaluating the effect of a dietetic supplement

International Journal of Biomathematics ◽

10.1142/s1793524519500864 ◽

2019 ◽

Vol 12 (08) ◽

pp. 1950086

Author(s):

Carlos N. Bouza-Herrera ◽

Sira M. Allende-Alonso ◽

Gajendra K. Vishwakarma ◽

Neha Singh

Keyword(s):

Body Mass Index ◽

Numerical Methods ◽

Sample Size ◽

Body Mass ◽

Optimal Allocation ◽

Computing Time ◽

Optimal Sample Size ◽

Optimum Sample Size ◽

Optimal Sample ◽

Sample Size Allocation

In many medical researches, it is needed to determine the optimal sample size allocation in a heterogeneous population. This paper proposes the algorithm for optimal sample size allocation. We consider the optimal allocation problem as an optimization problem and the solution is obtained by using Bisection, Secant, Regula–Falsi and other numerical methods. The performance of the algorithm for different numerical methods are analyzed and evaluated in terms of computing time, number of iterations and gain in accuracy using stratification. The efficacy of algorithm is evaluated for the response in terms of body mass index (BMI) to the dietetic supplement with diabetes mellitus, HIV/AIDS and cancer post-operatory recovery patients.

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