• Journal of Ecology and Environment
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• v.32 no.2
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• pp.67-73
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• 2009

Soil $CO_2$ efflux can vary markedly in magnitude over both time and space, and understanding this variation is crucial for the correct measurement of $CO_2$ efflux in ecological studies. Although considerable research has quantified temporal variability in this flux, comparatively little effort has focused on its spatial variability. To account for spatial heterogeneity, we must be able to determine the number of sampling points required to adequately estimate soil $CO_2$ efflux in a target ecosystem. In this paper, we report the results of a study of the number of sampling points required for estimating soil $CO_2$ efflux using a closed-dynamic chamber in young and old Japanese cedar plantations in central Japan. The spatial heterogeneity in soil $CO_2$ efflux was significantly higher in the mature plantation than in the young stand. In the young plantation, 95% of samples of 9 randomly-chosen flux measurements from a population of 16 measurements made using 72-$cm^2$ chambers produced flux estimates within 20% of the full-population mean. In the mature plantation, 20 sampling points are required to achieve means within $\pm$ 20% of the full-population mean (15 measurements) for 95% of the sample dates. Variation in soil temperature and moisture could not explain the observed spatial variation in soil $CO_2$ efflux, even though both parameters are a good predictor of temporal variation in $CO_2$ efflux. Our results and those of previous studies suggest that, on average, approximately 46 sampling points are required to estimate the mean and variance of soil $CO_2$ flux in temperate and boreal forests to a precision of $\pm$ 10% at the 95% confidence level, and 12 points are required to achieve a precision of $\pm$ 20%.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.405-414
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• 2012

In a stratified sample, the sampling frame is divided into non-overlapping groups or strata (e.g. geographical areas, age-groups, and genders). A sample is taken from each stratum, if this sample is a simple random sample it is referred to as stratified random sampling. In this paper, we study the bootstrap inference (including confidence interval) and test for a stratified population mean. We also introduce the bootstrap consistency based on limiting distribution related to the plug-in estimator of the population mean. We suggest three bootstrap confidence intervals such as standard bootstrap method, percentile bootstrap method and studentized bootstrap method. We also suggest a bootstrap test method computing the $ASL_{boot}$(Achieved Significance Level). The results of estimation are verified using simulation.

• Journal of Embryo Transfer
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• v.23 no.3
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• pp.203-209
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• 2008

To estimate the genetic characteristics and cumulative power of discrimination (CPD) existing among Hanwoo (Korean cattle) and exotic foreign population (Angus, Herford, Charolais, Holstein) we used a total of 414 genomic DNAs from five breeds population (Hanwoo, Angus, Hereford, Charolais, Holstein). Genetic characteristics indices including mean allele number among loci, unbiased heterozygosity ($h_i$) within locus and polymorphic information content (PIC) and unbiased average heterozygosity (H) among loci in four breeds were calculated using the generated allele frequencies by each marker. The mean allele numbers for all loci ranged between 5 and 7 while heterozygosity (H) ranged from 0.75 (HW) to 0.64 (HF) among loci and across breeds heterozygosity (H) was 0.69. The generated unbiased average heterozygosity among loci in each breed was integrated to the global formula of CPD resulting in 99.71 % within the populations. The genetic variation of HW (Hanwoo) showed highest estimates among the analyzed breeds.

• Asian Journal of Business Environment
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• v.12 no.3
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• pp.23-30
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• 2022

Purpose: This study aims to determine the effect of broadband access, education level, population numbers, and investment on the unemployment rate in Indonesia. Research design, data, and methodology: This study uses panel data from 34 provinces from 2016 to 2019. The analysis uses the fixed-effect model for panel data with the Feasible Generalized Least Square (FGLS) estimation method. Results: Broadband access has a negative and significant effect on the unemployment rate. Mean years of school, population, and foreign direct investment also have a negative and significant impact on the unemployment rate. In contrast, the domestic direct investment variable has a positive and significant effect. Conclusion: The availability of broadband access in an area allows easier and faster access to information. The ease of access to such information can affect producing goods and services, encouraging innovation and employment growth, and reducing the unemployment rate. This research recommends that the government intensify the Indonesia Broadband Plan policy to accelerate the development and equitable distribution of broadband access in all regions of Indonesia.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.377-385
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• 2015

After performing callback for nonresponses in sample survey, we present an estimator of regression form using an auxiliary variable and a variance estimator using replicate method. Parametric inference method of the response probability is also presented. We research an unbiased estimator of high efficiency for the population mean and a variance estimator with consistency under callback. We also prove the validity of the theory through the simulation.

• Journal of Environmental Impact Assessment
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• v.14 no.4
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• pp.157-164
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• 2005

Existing models that predict of Daily water supply include statistical models and neural network model. The neural network model was more effective than the statistical models. Only neural network model, which predict of Daily water supply, is focused on estimation of the operational control. Neural network model takes long learning time and gets into local minimum. This study proposes Neuro Genetic hybrid model which a combination of genetic algorithm and neural network. Hybrid model makes up for neural network's shortcomings. In this study, the amount of supply, the mean temperature and the population of the area supplied with water are use for neural network's learning patterns for prediction. RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). The comparison of the two models showed that the predicting capability of Hybrid model is more effective than that of neural network model. The proposed hybrid model is able to predict of Daily water, thus it can apply real time estimation of operational control of water works and water drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of the Ergonomics Society of Korea
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• v.11 no.1
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• pp.31-38
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• 1992

The population of aged people in industries has been increased remarkably due to the decline of birth rates and the increase of average life span. Also, the current automation trends makes aged people to work easy in industries since automation technologies help them to avoid physically hard tasks. Therefore the evaluation of aged workers' adaptability would be an important research topic. In the study, the mean and standard deviation of the reaction time are calculated to see the differences with four lamps's types and ages. It is found that the mean reaction time and the standard deviation are increased with the the increase of age. The quantitative approach provides an important information to be used not only for the adaptability estimation of aged workers but also for the working capability qualification for re-employment.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1423-1431
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• 2016

When the sample size in a certain domain is too small to produce adequate information, small area model with random effects is usually used. Also, if we do not consider an inherent pattern which data possess, it considerably affects inference. In this paper, we mainly focus on modeling to handle increased variation of the Current Population Survey (CPS) median income as the Internal Revenue Service (IRS) mean income increases. In a hierarchical Bayesian framework, most estimations are carried out through the Gibbs sampler while the grid method is used to generate parameters from non-standard form. Numerical study indicates that the performance of proposed model is better than that of CPS method in terms of four comparison measurements.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.

• International Journal of Railway
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• v.6 no.2
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• pp.45-52
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• 2013

Railway construction needs vast soil investigation for its infrastructure foundation designs along the planned railway path to identify the design parameters for stability and serviceability checks. The soil investigation data are usually classified and grouped to decide design input parameters per each construction section and budget estimates. Deterministic design method which most civil engineer and practitioner are familiar with has a clear limitation in construction/maintenance budget control, and occasionally produced overdesigned or unsafe design problems. Instead of using a batch type analysis with predetermined input parameters, data population collected from site soil investigation and design load condition can be statistically estimated for the mean and variance to present the feature of data distribution and optimized with a best fitting probability function. Probabilistic approach using entire feature of design input data enables to predict the worst, best and most probable cases based on identified ranges of soil and load data, which will help railway designer select construction method to save the time and cost. This paper introduces two Monte Carlo simulations actually applied on estimation of retaining wall external stability and long term settlement of organic soil in soil investigation area for a recent high speed railway project.