• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Survey Research
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• v.6 no.1
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• pp.39-62
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• 2005

In this paper we investigate some concepts frequently called in sample surveys such as 'representative of sample' as well as 'consistency', 'unbiasedness', and 'efficiency' in estimation. The first is strongly related with sampling procedure including coverage rate of survey population, response rate in establishment survey, and recruit rate of final samples. The others, however, are concerned with both sampling design and corresponding estimators simultaneously. Whereas both consistency and unbiasedness are based on the representative sample, efficiency does not depend on the representative sample. The representative of sample can be increased by raising the rate of coverage, response and recruit as well. Consistency may be investigated according to variables of interest and auxiliary variables. The well-known raing-ratio weighting method is a method to increase consistency of auxiliary variables by means of matching population size in each cell. Efficiency is not directly related with the representative of sample, and allocation methods such as proportional and Neyman allocation in stratified sampling and post-stratification are all methods to increase the efficiency of estimation under the condition of satisfying the representative of sample.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.121-123
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• 1996

Capacitor-run motor has a capacitor id series with the auxiliary winding for normal running connections. After the shape of stator and rotor are determined, the motor is designed with variables such as winding distributions or capacitance except punching variables. In this paper, the winding distribution and the turn ratio was taken as design variables because the winding distribution affects the torque and efficiency. And capacitance was selected as an additional variable. Simulation results show the validity of proposed method.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.97-105
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• 2013

Stratification (among other applications) is a popular technique used in survey practice to improve the accuracy of estimators. Its full potential benefit can be gained by the effective use of auxiliary variables in stratification related to survey variables. This paper focuses on the problem of stratum formation when multiple stratification variables are available. We first review a variance reduction strategy in the case of univariate stratification. We then discuss its use for multivariate situations in convenient and efficient ways using three methods: compromised measures of size, principal components analysis and a K-means clustering algorithm. We also consider three types of compromising factors to data when using these three methods. Finally, we compare their efficiency using data from MU281 Swedish municipality population.

• Journal of Preventive Medicine and Public Health
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• v.28 no.3 s.51
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• pp.663-677
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• 1995

Special health examination institute has done periodic health examination for workers who have worked in the hazardous workplace. However, assessment on outcome in special health examination institute about detection ability of occupational disease has not been. In this circumstances, we studied on the differences of health examination outcome among special health examination institutes and identified related factors which affected outcome of special health examination in the special health examination institutes. The summary of the results were as follows. 1. 50 special health examination institutes were examined in this study. Among them, university institutes were 13 cases(26.0%), hospitals were 20 cases(40.0%), a corporation aggregates were 9 cases(18.0%) and an auxiliary organs of company were 8 cases(16.0%). There were 29(58.0%) institutes with a preventive medicine specialist, but 21 institutes(42.0%) were not. 2. Total workers examined in 50 institutes were 606,948 and workers diagnosed as occupational disease$(D_1)$ were 3,156. The rate of occupational disease was 6 workers per 1,000 examined workers. Workers needed for close observation(C) were 95,809 and the rate of workers needed for close observation was 141 per 1,000 examined workers. 3. The rate of occupational disease of university institutes was highest(11.3 per 1,000 examined workers), and followed by hospitals(6.0 per 1,000 examined workers), a corporation aggregates(4.2 per 1,000 examined workers), and an auxiliary organs of company(1.2 per 1,000 examined workers). The difference of the rate of occupational disease between university institutes and an auxiliary organs of company was statistically moderate significant(p<.1). The rate of occupational disease in special health examination institutes with establishment duration was more than 10 years was statistically higher than institutes with establishment duration was less than 10 years(p<.1). 4. The results of multiple regression, $R^2$ was 0.3394(adjusted $R^2$ was 0.2109), F-value was 2.6416(p<.05), and statistically significant variables were establishment duration(p<.01), number of examined workers per one doctor(p<.1), and auxiliary organs of company(p<.1), which dependent variable was the rate of occupational disease and independent variables were number of examined workers per one doctor, classification of institute, the rate of working environment exceeding TLV, duration of institute establishment, presence of a preventive medicine specialist.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.06a
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• pp.57-70
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• 2001

In this paper we consider a various estimation methods including the post-stratification estimation, regression estimation and calibration estimation or a generalized raking estimation under a unit nonresponse. All of them have a common type of calibration estimation based on the post-stratification for a categorical auxiliary variables.

• Bulletin of the Korean Mathematical Society
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• v.36 no.2
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• pp.305-314
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• 1999

The Hsu-Robbins-erd s theorem states that if {$X_m,n\geq1$} is a sequence of independent and identically distributed random variables, then ${EX_1}^2<\infty$ and $EX_1$=0 if and only if ${\sum_{n=1}}^\infty\;P($\mid${\sum_{k=1}}^nX_k$\mid$\geqn\in)<\infty$ for every $\in$ > 0. Under some auxiliary conditions, Sp taru (1994) extended this to the case where the $X_n$ are independent, but their distributions come from a finite set. Pruss (1996) proved Sp taru's result under weaker conditions, The purpose of this paper is to improve Pruss conditions.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.427-438
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• 2021

Two-stage sampling allows us to estimate population characteristics by both unit and cluster level together. Given a complex auxiliary information, integrated calibration weighting would better reflect the level-wise characteristics as well as multivariate characteristics between levels. This paper explored the integrated calibration weighting methods by Estevao and Särndal (2006) and Kim (2019) through a simulation study, where the efficiency of those weighting methods was compared using an artificial population data. Two weighting methods among others are shown efficient: single step calibration at the unit level with stacked individualized auxiliary information and iterative integrated calibration at each level. Under both methods, cluster calibrated weights are defined as the average of the calibrated weights of the unit(s) within cluster. Both were very good in terms of the goodness-of-fit of estimating the population totals of mutual auxiliary information between clusters and units, and showed small relative bias and relative mean square root errors for estimating the population totals of survey variables that are not included in calibration adjustments.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.499-514
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• 2007

It is a common practice to use the auxiliary variables to impute missing values from item nonresponse in surveys. Sometimes there are few auxiliary variables for missing value imputation, but if spatial and time autocorrelations exist, we should use these correlations for better results. Recently, Lee et al. (2006) showed that spatial autocorrelation could be efficiently used for missing value imputation when spatial autocorrelation existed, using the data from the farm household economy data in Gangwon-do, 2002. In this paper, we present au evaluation of spatial and space-time nonresponse imputation methods when there exist spatial and time autocorrelations using the monthly data during 2000-2002 from the same data previously used by Lee et al. (2006). We show that space-time imputation method is more efficient than the other through the numerical simulations.

• Journal of Power System Engineering
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• v.17 no.1
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• pp.42-49
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• 2013

The global movie industry is continuing rapid growth through application of the latest technology. 3D movies are being produced and shown for a more effective viewing experience. Special chairs for audiences are being experimentally manufactured and installed for the greatest viewing effect. This special chair has a structure that applies vibrating stimuli to specific parts of the body by attaching vibration transducers to theater chairs and synchronizing it with each scene of the movie. In a previous study, it has been confirmed that we can analyze the vibration transfer characteristics of sponge seats through the application of an experimental modal analysis method and obtain design variables easily. In this paper, we examine the major design parameters needed in the development of a foaming sponge seat in which auxiliary springs are inserted to improve the vibration transfer effect of a chair seat. Through analyzing several prototypes by applying experimentation as well as the experimental modal analysis method, it was confirmed that the effect of vibration transfer can be improved through the use of an auxiliary member.