• Journal of Korean Society of Forest Science
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• v.65 no.1
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• pp.68-73
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• 1984

The purpose of this paper was to figure out the efficiencies of two sampling techniques, a simple random sampling and a two-stage P.P.S. (probability proportional to size) sampling, in estimating the volume of the mature coniferous stands near Salzburg, Austria. With black-and-white infrared photographs at a scale 1:10,000, the following four classes were considered; non-forest, young stands less than 40 years, mature beech and mature coniferous stands. After the classification, a field survey was carried out using a relascope with a BAF (basal area factor) 4. For the simple random sampling, 99 points were sampled, while for the P.P.S. sampling, 75 points were sampled in the mature coniferous stands. The following results were obtained. 1) The mean standing coniferous volume estimate was $422.0m^3/ha$ for the simple random sampling and $433.5m^3/ha$ for the P.P.S. sampling method. However, the difference was not statistically significant. 2) The required number of sampling points for a 5% sampling error were 170 for the two stage P.P.S. sampling, but 237 for the simple random sampling. 3) The two stage P.P.S. method reduced field survey time by 17% as compared to the simple random sampling.

• Transactions on Electrical and Electronic Materials
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• v.7 no.4
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.459-469
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• 1998

Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

• Journal of Korean Society of Forest Science
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• v.18 no.1
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• pp.1-7
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• 1973

This survey was estimated under the ratio estimate such as single class method, simple random sampling method, compound ratio sampling method, separate ratio sampling method and average tree sampling method artificial forest pitch pine volume. The following results were realized by the ratio estimates. At the above table simple random sampling method and compound ratio sampling method are the only ones which is included the actual stand volume in the ratio estimatedstand volume. It is thought that the sampling was in a such good result was because of stand structual stands were simple forest. The most simple measurement and calcuation on the stand volume estimates, in order, would be (1) single class method, (2) simple random sampling method (3) average tree method (4) separate ratio sampling method and compound ratio sampling method, and at the planted evenaged forest the method has realized the best results in obtaining good accuracy and the measure stand volume with least time, expenses and labor in considerably.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.1-11
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• 2001

In PPS sampling, measure of size(MOS) is used to determine the probability of selection of sampling unit. However, some large scale surveys conducted in NSO(National Statistical Office) showed that the sampling units have the similar MOS. In such case, simple cluster sampling method instead of PPS sampling is recommended to give the interviewers a similar work load. In this paper, MSE and CV of the above two sampling methods applied to the 1997 Economically Active Population Survey sample design are compared.

• Fisheries and Aquatic Sciences
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• v.21 no.8
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• pp.24.1-24.9
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• 2018

The Korean bottom trawl survey has been deployed on a regular basis for about the last decade as part of groundfish stock assessments. The regularity indicates that they sample groundfish once per grid cell whose sides are half of one latitude and that of one longitude, respectively, and whose inside is furthermore divided into nine nested grids. Unless they have a special reason (e.g., running into a rocky bottom), their sample location is at the center grid of the nine nested grids. Given data collected by the survey, we intended to show how to appropriately estimate not only the survey index of a fish stock but also its uncertainty. For the regularity reason, we applied the systematic sampling theory for the above purposes and compared its results with a reference, which was based on the simple random sampling. When using the survey data about 11 fish stocks, collected by the spring and fall surveys in 2014, the survey indices of those stocks estimated under the systematic sampling were overall more precise than those under the simple random sampling. In estimates of the survey indices in number, the standard errors of those estimates under the systematic sampling were reduced from those under the simple random sampling by 0.23~27.44%, while in estimates of the survey indices in weight, they decreased by 0.04~31.97%. In bias of the estimates, the systematic sampling was the same as the simple random sampling. Our paper is first in formally showing how to apply the systematic sampling theory to the actual data collected by the Korean bottom trawl surveys.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.229-240
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• 2005

This study described the application of constructivism and information technology for teaching simple random sampling. We considered more student's participation, more interaction, and more flow in their introductory statistics class. In addition, we presented a web-based teaching and learning system for simple random sampling to demonstrate.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.5
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• pp.594-604
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• 2017

The Korean National Institute of Fisheries Science (NIFS) has biannually (spring and fall, respectively) deployed a bottom trawl survey along the coastal areas for last decade, taking samples on a regular basis (i.e., a systematic sampling). Despite the availability of the survey data, NIFS has not yet officially reported the estimates of the groundfish population sizes as well as has not evaluated uncertainty of the estimates. The objectives of our study were to infer the relative size of a fish population, applying two different sampling techniques (namely simple and stratified sampling) with different observation units to the NIFS survey data, and to compare those two techniques in bias and precision. For demonstration purposes, we used data on Pacific cod (Gadus macrocephalus) collected by the 2011-2015 surveys, and the results of simple and stratified sampling showed that the point estimates and precision varied by observation unit as well as the sampling technique.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.153-165
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• 2001

In this paper we point out another advantage of systematic sampling over simple random sampling, which have not yet been spelled out in the literature. After a single sample is drawn by a sampling scheme, it is important to check whether the achieved sample represents the pupulation well or out. Therefore, a sampling scheme which avoids the possibility of selecting non-preferred samples is desirable. The simulation results are given to illustrate that, in the ordered population, the possibility of selecting non-preferred sample by systematic sampling is lower than that by simple random sampling.

• Survey Research
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• v.2 no.2
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• pp.153-165
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• 2001

In this paper we point out another advantage of systematic sampling over simple random sampling, which have not yet been spelled out in the literature. After a single sample is drawn by a sampling scheme, it is important to check whether the achived sample represents the population well or not. Therefore. a sampling scheme which avoids the possibility of selecting non-preferred samples is desirable. The simulation results are given to illustrate that, in the ordered population, the possibility of selecting non-preferred sample by systematic sampling is lower than that by simple random sampling.