• 대한원격탐사학회지
• /
• 제18권3호
• /
• pp.155-162
• /
• 2002

Neural networks have been focused on as a robust classifier for the remotely sensed imagery due to its statistical independency and teaming ability. Also the artificial neural networks have been reported to be more tolerant to noise and missing data. However, unlike the conventional statistical classifiers which use the statistical parameters for the classification, a neural network classifier uses individual training sample in teaming stage. The training performance of a neural network is know to be very sensitive to the discrepancy of the number of the training samples of each class. In this paper, the effect of the population discrepancy of training samples of each class was analyzed with three layered feed forward network. And a method for reducing the effect was proposed and experimented with Landsat TM image. The results showed that the effect of the training sample size discrepancy should be carefully considered for faster and more accurate training of the network. Also, it was found that the proposed method which makes teaming rate as a function of the number of training samples in each class resulted in faster and more accurate training of the network.

• Journal of the Korean Statistical Society
• /
• 제30권3호
• /
• pp.347-366
• /
• 2001

Robust analogues of the likelihood ratio test are considered for testing of hypotheses involving multiple discrete distributions. The test statistics are generalizations of the Hellinger deviance test of Simpson(1989) and disparity tests of Lindsay(1994), obtained by looking at a 'penalized' version of the distances; harris and Basu (1994) suggest that the penalty be based on reweighting the empty cells. The results show that often the tests based on the ordinary and penalized distances enjoy better robustness properties than the likelihood ratio test. Also, the tests based on the penalized distances are improvements over those based on the ordinary distances in that they are much closer to the likelihood ratio tests at the null and their convergence to the x$^2$ distribution appears to be dramatically faster; extensive simulation results show that the improvement in performance of the tests due to the penalty is often substantial in small samples.

• Communications for Statistical Applications and Methods
• /
• 제18권5호
• /
• pp.645-655
• /
• 2011

Chandrasekar et al. (2004) introduced a generalized Type-II hybrid censoring. In this paper, we propose generalized doubly Type-II hybrid censoring. In addition, this paper presents the statistical inference on the scale parameter for the half logistic distribution when samples are generalized doubly Type-II hybrid censoring. The approximate maximum likelihood(AMLE) method is developed to estimate the unknown parameter. The scale parameter is estimated by the AMLE method using two di erent Taylor series expansion types. We compar the AMLEs in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20; 30; 40 and various censored samples. The $AMLE_I$ is better than $AMLE_{II}$ in the sense of the MSE.

• Communications for Statistical Applications and Methods
• /
• 제13권2호
• /
• pp.419-428
• /
• 2006

The method of Reverse Ranked Ordering-Set Sampling(RROSS) as an opposed Ranked Ordering-Set Sampling(ROSS) and Ranked-Set Sampling(RSS) is discussed. We propose the test statistic using sign test on RROSS. This method is effective when observations are expensive and measurement is perhaps destructive or invasive. This method obtains more informations than ROSS and RSS. The asymptotic relative efficiencies of RROSS with respect to ROSS and RSS are always greater than 1 for all sample sizes. We consider a simple model to describe the effect of imperfect judgment errors.

• 천문학회보
• /
• 제44권2호
• /
• pp.70.2-70.2
• /
• 2019

Recent observational works have reported spiral wave patterns (SWPs) in sunspots, but there is a lack of samples to derive the physical properties. In this presentation, we suggest the automatic method to detect the SWPs in observational data and present their statistical properties. From our method, we find more than 1000 SWPs observed by the Atmospheric Imaging Assembly onboard in the Solar Dynamic Observatory from 2013 to 2018. From our samples, more than half of the SWPs has the one spiral arm. The predominant oscillation period is 2 to 3 minutes. The rotating direction of the spiral arms does not depend on the latitude and the polarity of the sunspots. Our statistical results support the physical model suggested by Kang et al. (2019) that explain the generation of SWPs as the depth of the wave driving source and azimuthal modes in the straight vertical magnetic flux tube.

• Communications for Statistical Applications and Methods
• /
• 제22권3호
• /
• pp.223-232
• /
• 2015

The area under the ROC curve (AUC), the volume under the ROC surface (VUS) and the hypervolume under the ROC manifold (HUM) are defined and interpreted with probability that measures the discriminant power of classification models. AUC, VUS and HUM are expressed with the summation and integration notations for discrete and continuous random variables, respectively. AUC for discrete two random samples is represented as the nonparametric Mann-Whitney statistic. In this work, we define conditional Mann-Whitney statistics to compare more than two discrete random samples as well as propose that VUS and HUM are represented as functions of the conditional Mann-Whitney statistics. Three and four discrete random samples with some tie values are generated. Values of VUS and HUM are obtained using the proposed statistic. The values of VUS and HUM are identical with those obtained by definition; therefore, both VUS and HUM could be represented with conditional Mann-Whitney statistics proposed in this paper.

• 대한수학회지
• /
• 제50권4호
• /
• pp.879-897
• /
• 2013

Shannon entropy is one of the widely used randomness measures especially for cryptographic applications. However, the conventional entropy tests are less sensitive to the inter-bit dependency in random samples. In this paper, we propose new online randomness test schemes for true random number generators (TRNGs) based on the mutual information between consecutive ${\kappa}$-bit output blocks for testing of inter-bit dependency in random samples. By estimating the block entropies of distinct lengths at the same time, it is possible to measure the mutual information, which is closely related to the amount of the statistical dependency between two consecutive data blocks. In addition, we propose a new estimation method for entropies, which accumulates intermediate values of the number of frequencies. The proposed method can estimate entropy with less samples than Maurer-Coron type entropy test can. By numerical simulations, it is shown that the new proposed scheme can be used as a reliable online entropy estimator for TRNGs used by cryptographic modules.

• Communications for Statistical Applications and Methods
• /
• 제13권1호
• /
• pp.151-165
• /
• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.

• 영재교육연구
• /
• 제21권2호
• /
• pp.287-307
• /
• 2011

본 연구에서는 일반학급 학생들과의 비교를 통해 수학영재학급 학생들의 표본 개념 이해 수준을 살펴본다. 먼저 조사 과제에 대한 학생들의 반응을 토대로 표본 개념 이해 수준을 평가하기 위한 기준을 개발하였다. 학생들의 반응을 분석한 결과 표본이 모집단의 일부분이라는 것에 대한 인식이 부족한 0수준, 표본을 모집단의 부분집합으로 인식하는 1수준, 표본을 모집단의 준비례적 축소버전으로 인식하는 2수준, 편의없는 표본의 중요성을 인식하는 3수준, 무작위 추출이 표본에 미치는 영향을 이해하는 4수준으로 구분할 수 있었다. 개발된 평가 기준을 근거로 각 학생의 이해 수준을 조사한 후, 수학영재학급 학생들과 일반학급 학생들의 표본에 대한 이해 수준의 차이를 알아보기 위해 두 독립표본 t 검정을 실시하였다. 검정결과 초등학교와 중학교 모두에서 수학영재학급 학생들과 일반학급 학생들 두 그룹 간에 통계적으로 유의한 차이가 있는 것으로 나타났다. 그러나 수준별 빈도를 조사한 결과 수학영재학급 학생들의 이해 수준이 상위 수준에 분포되기보다는 일반학급 학생들의 이해 수준과 상당부분 중첩됨을 확인할 수 있었다.

• Journal of the Korean Statistical Society
• /
• 제25권1호
• /
• pp.25-37
• /
• 1996

In this paper, we propose regression estimators based on a partial replacement sampling scheme over two successive occasions and derive the minimum variances of them. PPSWR, RHC, $\pi$PS and PPSWOR schemes are considered to select unequal probability samples on two occasions. Simulation results over four populations are given for comparison of composite estimators and regression estimators.