• Communications for Statistical Applications and Methods
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• 제18권2호
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• pp.155-164
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• 2011

This paper suggests two ratio-cum-product estimators of finite population mean using known coefficient of variation and co-efficient of kurtosis of auxiliary characters. The bias and mean squared error of the proposed estimators with large sample approximation are derived. It has been shown that the estimators suggested by Upadhyaya and Singh (1999) are particular case of the suggested estimators. Almost ratio-cum product estimators of suggested estimators have also been obtained using Jackknife technique given by Quenouille (1956). An empirical study is also carried out to demonstrate the performance of the suggested estimators.

• Transactions of the Korean Society of Automotive Engineers
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• 제9권2호
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• pp.152-167
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• 2001

Recently safety systems for the commercial vehicle have been rapidly developed. However, we still have many problems in the vehicle stability and the braking performance. Especially, a commercial vehicle may meet a dangerous braking condition when the vehicle is lightly loaded or empty and when the road is wet or slippery. Under these conditions, the truck can spin out or the tractor can jackknife or the trailer can swing out. To design the air brake system for the commercial vehicle, since the air brake system has many design variables, there must have been intensive researches on a method how to prevent dynamic instability and how to maximize the vehicle deceleration. In this study, mathematical models about the tractor-semitrailer and the air brake system including an ABS controller have been constructed for computer simulation. Also, simple examples are applied to show the usefulness of the program. Designers can use this simulation program for understanding the braking characteristics such as trajectory, braking distance, longitudinal deceleration, lateral deceleration, and yaw rate on various road conditions.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.189-200
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• 2013

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.443-449
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• 2017

Estimation of population proportion like the distribution rate of LED TV and the prevalence of a disease are often estimated based on survey sample data. Population proportion is generally considered as a special form of population mean. In complex sampling like stratified multistage sampling with unequal probability sampling, the denominator of mean may be random variable and it is estimated like ratio estimator. In this research, we examined the estimation of distribution rate based on stratified multistage sampling, and determined some numerical outcomes using stratified random sample data with about 25% of missing observations. In the data used for this research, the survey weight was determined by deterministic way. So, the weights are not random variable, and the population distribution rate and its variance estimator can be estimated like population mean estimation. When the weights are not random variable, if one estimates the variance of proportion estimator using ratio method, then the variances may be inflated. Therefore, in estimating variance for population proportion, we need to examine the structure of data and survey design before making any decision for estimation methods.

• Journal of the military operations research society of Korea
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• 제12권2호
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• pp.68-81
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• 1986

This thesis studies the estimation from interarrival and service time data of the mean number of customers in service at time t for an $M/G/{\infty}$ queue. The assumption is that the parametric form of the service time distribution is unknown and the empirical distribution of twe service time is used in the estimate the mean number of customers in service. In the case in which the customer arrival rate is known the distribution of the estimate is derived and an approximate normal confidence interval procedure is suggested. The use of the nonparametric methods, which are the jackknife and the bootstrap, to estimate variability and construct confidence intervals for the estimate is also studied both analytically and by simulation.

• The Korean Journal of Applied Statistics
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• 제6권2호
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• pp.329-339
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• 1993

Kernel regression is a nonparametric regression technique which requires only differentiability of the true function. If one wants to use the kernel regression technique to produce smooth estimates of a curve over a finite interval, one can realize that there exist distinct boundary problems that detract from the global performance of the estimator. This paper develops a kernel to handle boundary problem. In order to develop the boundary kernel, a generalized jacknife method by Gray and Schucany (1972) is adapted. Also, it will be shown that the boundary kernel has the same order of convergence rate as non-boundary.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.365-379
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• 2005

Balanced Repeated Replication(BRR) is widely used to estimate the variance of linear or nonlinear estimators from complex sampling surveys. Most of survey data sets include imputed missing values and treat the imputed values as observed data. But applying the standard BRR variance estimation formula for imputed data does not produce valid variance estimators. Shao, Chen and Chen(1998) proposed an adjusted BRR method by adjusting the imputed data to produce more accurate variance estimators. In this paper, another adjusted BRR method is proposed with examples of real data.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.583-595
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• 2004

For the Korean Economically Active Population Survey(EAPS), we consider the composite estimator based on logistic regression model to estimate the unemployment rate for small areas(Si/Gun). Also, small area estimation technique based on hierarchical generalized linear model is proposed to include the random effect which reflect the characteristic of the small areas. The proposed estimation techniques are applied to real domestic data which is from the Korean EAPS of Choongbuk. The MSE of these estimators are estimated by Jackknife method, and the efficiencies of small area estimators are evaluated by the RRMSE. As a result, the composite estimator based on logistic model is much more efficient than others and it turns out that the composite estimator can produce the reliable estimates under the current EAPS system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• 제19권4호
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• pp.361-374
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• 2007

This study presents long-term shoreline changes of barrier islands in Nakdong Estuary using aerial photographs. Digital photogrammetry is used for constructing mosaic aerial photographs, which yield six sets of shoreline data ranging from 1975 to 2001. Three kinds of rate of shoreline changes such as EPR (End Point Rate), JKR(Jackknife Rate) and LRR (Linear Regression Rate) are computed by a GIS-based Digital Shoreline Analysis Systems. There have been remarkable changes both in Sinja Island and Doyodeung. Western part of Sinja Island advanced seaward, whereas eastern part retreated landward, giving appearance that the island rotated counterclockwise. Rate of shoreline changes at both ends reach 20 m/yr. Doyodeung occurred newly in front of Baekhapdeung in 1993, resulting in shoreline advance in a rate of 40 m/yr. Rate of shoreline changes differ both within and between barrier islands and have a tendency to increase eastward. To understand this spatial variability of rate of shoreline changes, it is suggested to make a detailed investigation into the impact of coastal development on hydrodynamic and sedimentary processes.

• Korean Journal of Remote Sensing
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• 제15권2호
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.