• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.783-791
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• 2008

Estimation procedure of the finite population proportion and distribution function is considered. Based on a logistic regression model, an approximately model- optimal estimator is defined and conditions for the estimator to be design-consistent are given. Simulation study shows that the model-optimal design-consistent estimator defined under a logistic regression model performs well in estimating the finite population distribution function.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.83-92
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• 2020

The purpose of this work was to apply geographical information system (GIS) for geostatistical analyzing by selecting a semi-variogram model to quantify the spatial correlation of the population distribution with residential neighborhoods in the both sides of Mosul city. Two hundred and sixty-eight sample sites in 240 ㎢ are adopted. After determining the population distribution with respect to neighborhoods, data were inserted to ArcGIS10.3 software. Afterward, the datasets was subjected to the semi-variogram model using ordinary kriging interpolation. The results obtained from interpolation method showed that among the various models, Spherical model gives best fit of the data by cross-validation. The kriging prediction map obtained by this study, shows a particular spatial dependence of the population distribution with the neighborhoods. The results obtained from interpolation method also indicates an unbalanced population distribution, as there is no balance between the size of the population neighborhoods and their share of the size of the population, where the results showed that the right side is more densely populated because of the small area of residential homes which occupied by more than one family, as well as the right side is concentrated in economic and social activities.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.407-416
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• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• Journal of Ecology and Environment
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• v.38 no.4
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• pp.493-503
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• 2015

This study analyzed how spatial distribution of Himantormia lugubris is affected by the microenvironment in the Antarctic Specially Protected Area (ASPA) No. 171 located in the Barton Peninsula of King George Island that belongs to the maritime Antarctic. In order to determine the population structure of H. lugubris growing in Baekje Hill within ASPA No. 171, we counted the individuals of different size groups after dividing the population into 5 growth stages according to mean diameter as follows: ≤ 1 cm, 1-3 cm, 3-5 cm, 5-10 cm, and ≥ 10 cm. The count of H. lugubris individuals in each growth stage was converted into its percentage with respect to the entire population, which yielded the finding that stages 1 through 5 accounted for 32.8%, 25.3%, 15.9%, 22.5%, and 3.5%, respectively. This suggests that the population of H. lugubris in ASPA No. 171 has a stable reverse J-shaped population structure, with the younger individuals outnumbering mature ones. The mean density of H. lugubris was 17.6/0.25 m², mean canopy cover 13.3%, and the mean dry weight 37.8 g/0.25 m². It began to produce spore in the sizes over 3 cm, and most individuals measuring 5-10 cm were adults with sexually mature apothecia. The spatial distribution of H. lugubris was highly heterogeneous. The major factors influencing its distribution and performance were found to be the period covered by snow, wind direction, moisture, size of the substrate, and canopy cover of Usnea spp. Based on these factors, we constructed a prediction model for estimating the spatial distribution of H. lugubris. Conclusively, the major factors for the spatial distribution of H. lugubris were snow, wind, substrate and the competition with Usnea spp. These results are important for understanding of the distribution in the maritime Antarctic and evolution of H. lugubris that claims a unique life history and ecological niche.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.3
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• pp.351-365
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• 2019

This study aims to predict the changes in population distribution in Korea by considering spatio-temporal characteristics of major migration reasons. For the purpose, we analyze the spatio-temporal characteristics of each major migration reason(such as job, family, housing, and education) and estimate the transition probability, respectively. By appling Markov chain model processes with the ChapmanKolmogorov equation based on the transition probability, we predict the changes in the population distribution for the next six years. As the results, we found that there were differences of population changes by regions, while there were geographic movements into metropolitan areas and cities in general. The methodologies and the results presented in this study can be utilized for the provision of customized planning policies. In the long run, it can be used as a basis for planning and enforcing regionally tailored policies that strengthen inflow factors and improve outflow factors based on the trends of population inflow and outflow by region by movement factors as well as identify the patterns of population inflow and outflow in each region and predict future population volatility.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.171-176
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• 2003

This paper deals with the problem of predicting order statistics in samples from a Rayleigh population when an outlier is present. Bayesian predictive distribution and prediction bounds of the p-th order statistics is obtained where an outlier of type $\theta\delta$ is present. In this connection, some identies are derived.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.368-383
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• 2014

Climate change is expected to affect population density, phenology, distribution, morphological traits, reproduction and genetics of insects, and even in the extinction of insects. To develop novel research subjects for predicting climate change effect, basic information about biological and ecological data on insect species should be compiled and reviewed. For this reason, this study was conducted to collect the biological information on insect pests that are essential for predicting potential damage caused by insect pests in future environment. In addition, we compared domestic and foreign research trends regarding climate change effect and suggested future research subjects. Domestic researchers were rather narrow in the subject, and were mostly conducted based on short-term monitoring data to determine relationship between insects and environmental variables. On the other hand, foreign researches studied on various subjects to analyze the effect of climate change, such as changes in distribution of insect using long-term monitoring data or their prediction using population parameters and models, and monitoring of the change of the insect community structure. To determine change of the phenology, distribution, overwintering characteristics, and genetic structures of insects under climate change through development of monitoring technique, in conclusion, further researches are needed. Also, development of population models for major or potential pests is important for prediction of climate change effects.

• Korean Journal of Agricultural Science
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• v.42 no.4
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• pp.397-406
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• 2015

Quantitative traits are mostly controlled by a large number of genes. Some of these genes tend to have a large effect on quantitative traits in cattle and are known as major genes primarily located at quantitative trait loci (QTL). The genetic merit of animals can be estimated by genomic selection, which uses genome-wide SNP panels and statistical methods that capture the effects of large numbers of SNPs simultaneously. In practice, the accuracy of genomic predictions will depend on the size and structure of reference and training population, the effective population size, the density of marker and the genetic architecture of the traits such as number of loci affecting the traits and distribution of their effects. In this review, we focus on the structure of Hanwoo reference and training population in terms of accuracy of genomic prediction and we then discuss of genetic architecture of intramuscular fat(IMF) and marbling score(MS) to estimate genomic breeding value in real small size of reference population.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.71-77
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• 1997

S independent sample 0,1,2, $\cdots$, s-1 (or stages 0,1,2, $\cdots$, s-1) are available from the Raleigh population. Procedure for predicting any order statistic in the $(s+1)^{th}$ sample is developed by obtaining the predictive distribution at stage s. Bounds for the sample size at stage S, in order to have the variance at stage S less than that at stage (s-1), are obtained.