• Carbon letters
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• v.4 no.1
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• pp.10-13
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• 2003

The structural studies of amorphous isotropic carbon prepared from pyrolysis of phenol formaldehyde resin have been carried out using X-ray diffraction. X-ray diffraction from as prepared sample at $1000^{\circ}C$ and a sample treated at $1900^{\circ}C$ revealed that both are amorphous even though there are small differences in short range order. It is found that both are graphite like carbon (GLC) with predominantly $sp^2$ hybridization. Small angle X-ray scattering results show that as prepared sample mainly consists of thin two dimensional platelets of graphitic carbon whereas they grow in thickness to become three dimensional materials of nano dimensions.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.193-198
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• 1999

The estimation of parameters in regression models with multiplicative errors is usually based on the gamma or log-normal likelihoods. Under reciprocal misspecification, we compare the small sample efficiencies of two sets of estimators via a Monte Carlo study. We further consider the case where the errors are a random sample from a Weibull distribution. We compute the asymptotic relative efficiency of quasi-likelihood estimators on the original scale to least squares estimators on the log-transformed scale and perform a Monte Carlo study to compare the small sample performances of quasi-likelihood and least squares estimators.

• Physical Therapy Korea
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• v.23 no.2
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• pp.93-99
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• 2016

Background: Parametric statistical procedures are typically conducted under the condition in which a sample distribution is statistically identical with its population. In reality, investigators use inferential statistics to estimate parameters based on the sample drawn because population distributions are unknown. The uncertainty of limited data from the sample such as lack of sample size may be a challenge in most rehabilitation studies. Objects: The purpose of this study is to review the bootstrapping method to overcome shortcomings of limited sample size in rehabilitation studies. Methods: Articles were reviewed. Results: Bootstrapping method is a statistical procedure that permits the iterative re-sampling with replacement from a sample when the population distribution is unknown. This statistical procedure is to enhance the representativeness of the population being studied and to determine estimates of the parameters when sample size are too limited to generalize the study outcome to target population. The bootstrapping method would overcome limitations such as type II error resulting from small sample sizes. An application on a typical data of a study represented how to deal with challenges of estimating a parameter from small sample size and enhance the uncertainty with optimal confidence intervals and levels. Conclusion: Bootstrapping method may be an effective statistical procedure reducing the standard error of population parameters under the condition requiring both acceptable confidence intervals and confidence level (i.e., p=.05).

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.451-460
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• 2008

This paper reviews nonparametric statistics by Neyman-Pearson test and Fisher test. Nonparametric statistics deal with the small sample with distribution-free assumption in multi-product and small-volume production. Two tests for various nonparametric statistic methods such as sign test, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Mood test, Friedman test and run test are also presented with the steps for testing hypotheses and test of significance.

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.80-87
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• 1990

In reliability and life testing the mean residual life (MRL) of an item plays a significant role. While there has been a great deal of discussion on the theoretical aspects of the MRL, good estimators of MRL have been difficult to obtain. In this paper we propose a new estimator of the MRL of items at a given age, which is especially good for a small sample. The new estimator compares favorably with the empirical MRL estimator for small samples.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.83-90
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• 2000

The chi-squared test statistic is usually employed for testing independence of two categorical variables in a two-way contingency table. It is well known that, under independence, the test statistic has an asymptotic chi-squared distribution under multinomial or product-multinomial models. For the case where both margins fixed, the sampling model of the contingency table is a multiple hypergeometric distribution and the chi-squared test statistic follows the same limiting distribution. In this paper, we study the difference between the small sample and large sample distributions of the chi-squared test statistic for the case with fixed margins. For a few small sample cases, the exact small sample distribution of the test statistic is directly computed. For a few large sample sizes, the small sample distribution of the statistic is generated via a Monte Carlo algorithm, and then is compared with the large sample distribution via chi-squared probability plots and Kolmogorov-Smirnov tests.

• Journal of Preventive Medicine and Public Health
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• v.15 no.1
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• pp.111-114
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• 1982

To determine Pb level in blood, we usually .used to pull out about 5ml blood from venous vessel and this sample was digested with acids to decompose organic matter and then determined the Pb contents by Atomic Absorption Spectrophotometer with flame. But recent trend in quan titating Pb in small amount of sample is very much recommended in clinical chemistry specially pediatrics, and industrial hygiene and occupational health area. Authors tried to determine Pb contents in small amount blood of $25{\mu}l$ by using capillary tube method and got the possibility of determination of ng amount of Pb in $25{\mu}l$ whole blood sample without any pretreatment of sample.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.2
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• pp.55-63
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• 1980

To study run-off characteristics in the small watersheds in Korea, investigations had been carried out for a period of 4 years from 1972 to 1975 in the sample watersheds. The samples were selected in four major river basins such as the Han River, the Keum River, the Nakdong River and the Yongsan River. Water levels and rainfall data had been. collected from each sample area where the measuring instruments were installed. The findings of this investigation can be summarized as follows; 1. With an average runoff rate of 60% in the sample watersheds, the average runoff rate. in each sample proved to be as below; the Han River Basin : 41.4% the Keum River Basin : 61.7% the Nakdong River Basin : 69.4% the Yong San River Basin : 69.2% 2. The base flow rate in the sample watersheds proved to be 8.1 mm/month. 3. A comparison of the runoff obtained from actual measurements made and that calculated by the Kaijyama formula showed that the latter is 9.1% lower than the former.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.517-529
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• 1998

When the sample size is small the robust minimum Hellinger distance (HD) estimator can have substantially poor relative efficiency at the true model. Similarly, approximating the exact null distributions of the ordinary Hellinger distance tests with the limiting chi-square distributions can be quite inappropriate in small samples. To overcome these problems Harris and Basu (1994) and Basu et at. (1996) recommended using a modified HD called penalized Hellinger distance (PHD). Lindsay (1994) and Basu et al. (1997) showed that another density based distance, namely the negative exponential disparity (NED), is a major competitor to the Hellinger distance in producing an asymptotically fully efficient and robust estimator. In this paper we investigate the small sample performance of the estimates and tests based on the NED and penalized NED (PNED). Our results indicate that, in the settings considered here, the NED, unlike the HD, produces estimators that perform very well in small samples and penalizing the NED does not help. However, in testing of hypotheses, the deviance test based on a PNED appears to achieve the best small-sample level compared to tests based on the NED, HD and PHD.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.172-173
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• 2008

LDA를 이용한 얼굴 인식에서 발생하는 small sample sire 문제를 해결하기 위해서 regularization method를 주로 사용한다. 이 방법을 사용하게 되면 클래스 내 분산행렬의 특이성을 없앨 수 있지만, 클래스 내 분산행렬과 단위행렬 $\alpha$를 곱한 값을 더하는 과정에서 $\alpha$의 값을 임의적으로 정해주어야 되고 이 값에 따라 인식률이 개선되지 않을 수 있다는 문제점이 있다. Resampling 개념을 이용하여 학습 데이터의 수를 늘리게 되면 regularization method보다 개선된 인식률을 얻을 수 있다. 또한 경험적으로 $\alpha$값을 정해 주어야 하고, $\alpha$값에 따라 인식률의 변통이 생길 수 있는 단점이 개선되는 효과를 얻을 수 있다.