• Journal of Environmental Impact Assessment
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• v.30 no.3
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• pp.155-163
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• 2021

The number of trees to be removed trees (ART) in the environmental impact assessment is an environmental indicator used in various parts such as greenhouse gas emissions and waste of forest trees calculation. Until now, the ART has depended on the forest tree density of the vegetation survey, and the uncertainty of estimating the amount of removed trees has increased due to the sampling bias. A full-scale survey can be offered as an alternative to improve the accuracy of ART, but the reality is that it is impossible. As an alternative, there is an individual tree detection using aerial image (ITD), and in this study, we compared the ARTs estimated by full-scale survey, sample survey, and ITD. According to the research results, compared to the result of full-scale survey, the result of ITD was overestimated by 25. While 58 were overestimated by the sample survey (average). However, as the sample survey is an estimate based on random samples, ART will be overestimated or underestimated depending on the number and size of quadrats.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.289-304
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• 2001

2001년에 실시할 $\ulcorner$국민건강.영양조사$\lrcorner$를 위한 새로운 표본설계를 하였다. 본 표본설계에서는 표본의 대표성을 높이기 위해서 기존의 표본가구수는 유지하면서 표본조사구를 증가시키고 새로운 지역 층화변수를 추가로 도입하였다. 또한 추정량의 추정오차 공식을 유도하여 추정의 신뢰성을 측정할 수 있도록 설계하였다.

• Journal of the Korean Statistical Society
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• v.8 no.2
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• pp.165-168
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• 1979

For crops that are planted in a lattice layout, sampling designs can be made to take advantage of this regular arrangement. In order to select which tobacco plants to be examined in a survey to estimate disease loss in tobacco a method of, so called, bent plots was devised based on the regularity of plantings in the tobacco fields. We will first describe this sample selection and measurement method and then provide estimators and their bias and variance properties.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.755-760
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• 1999

In multivariate stratified sampling surveys it is general to use a few stratification variables which are highly correlated with the important variables at design stage. But there might be some secondary study variables which are not so highly correlated with those stratification variables. In that case it is not efficient to use the same type of estimator due to the secondary variables as the one base on the important variables. A post-stratified estimation is proposed to increase the efficiency of the estimator with existence of secondary variables. The proposed method is illustrated with a set of fishery household population survey data.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.137-148
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• 1991

This paper describes the design of sample of the survey on the trend of house prices in city areas. The purpose of this research is to increase the precision of house price index in 39 cities and to provide with an accurate house price indes. The sample is selected in the stratified two stage sampling. In chapter 2, review and discussions are given on the sample design now in use. In chapter 3, we describe the sample size and the stratification, the house price index and error, and the substitution of sample. Finally we consider on problems of the sample design and some alternatives to solve them.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.443-456
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• 2013

Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.247-256
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• 2003

In this study, we systemize the theoretical validity for applying RRM to three-stage cluster sampling method and derive the estimate and it's variance of sensitive parameter. We derive the minimum variance form under the optimal values of the subsample sizes when the costs are fixed. Under the some given precision, we obtain the optimal values of the subsample sizes and derive the minimum cost form by using them. We apply the three-stage cluster RRM to field survey and suggest some necessary points for practical use.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.673-687
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• 2005

We investigate both the design-based and model-based inferences, which are usual inferential methods in survey sampling. While the design-based inference is on the basis of randomization principle, The motel-based inference is based on likelihood principle as well as conditionality principle. There have been some disputes between two inferences for a long time and those have not yet been determined. In this paper we reviewed some issues on two inferences and compared their advantages and disadvantages in some viewpoints.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.483-488
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• 2014

Aim: This study employed public use National Health and Nutrition Examination Survey (NHANES III) data to investigate the association between urinary cadmium (UDPSI) and all cause, all cancer and prostate cancer mortalities in men. Patients and Methods: NHANES III household adult, laboratory and mortality data were merged. The sampling weight used was WTPFEX6, with SDPPSU6 applied for the probability sampling unit and SDPSTRA6 to designate the strata for the survey analysis. Results: For prostate cancer death, the significant univariates were UDPSI, age, weight, and drinking. Under multivariate logistic regression, the significant covariates were age and weight. For all cause mortality in men, the significant covariates were UDPSI, age, and poverty income ratio. For all cancer mortality in men, the significant covariates were UDPSI, age, black and Mexican race. Conclusions: UDPSI was a predictor of all cause and all cancer mortalities in men as well as prostate cancer mortality.