• Communications for Statistical Applications and Methods
• /
• 제9권3호
• /
• pp.853-864
• /
• 2002

In the analysis of categorical data subject to misclassification errors, the observed cell proportions are adjusted by a misclassification probabilities and estimates of variances are adjusted accordingly. In this case, it is important to determine the extent to which misclassification probabilities are homogeneous within a population. This paper considers methods to evaluate the power of chi-squared tests for homogeneity with complex survey data subject to misclassification errors. Two cases are considered: adjustment with homogeneous misclassification probabilities; adjustment with heterogeneous misclassification probabilities. To estimate misclassification probabilities, logistic regression method is considered.

• Journal of the Korean Statistical Society
• /
• 제36권2호
• /
• pp.275-284
• /
• 2007

Confidence intervals for the population proportion in a double sampling scheme subject to false-positive misclassification are considered. The confidence intervals are obtained by applying Agresti and Coull's approach, so-called "adding two-failures and two successes". They are compared in terms of coverage probabilities and expected widths with the Wald interval and the confidence interval given by Boese et al. (2006). The latter one is a test-based confidence interval and is known to have good properties. It is shown that the Agresti and Coull's approach provides a relatively simple but effective confidence interval.

• Journal of the Korean Statistical Society
• /
• 제27권4호
• /
• pp.471-483
• /
• 1998

The influence of observations on the misclassification probability in multiple discriminant analysis under the equal covariance assumption is investigated by the local influence method. Under an appropriate perturbation we can get information about influential observations and outliers by studying the curvatures and the associated direction vectors of the perturbation-formed surface of the misclassification probability. We show that the influence function method gives essentially the same information as the direction vector of the maximum slope. An illustrative example is given for the effectiveness of the local influence method.

• Journal of the Korean Data and Information Science Society
• /
• 제27권1호
• /
• pp.217-223
• /
• 2016

Outcome misclassification is widespread in classification problems, but methods to account for it are rarely used. In this paper, the problem of inference with misclassified multinomial logit data with a large number of multinomial parameters is addressed. We have had a significant swell of interest in the development of novel methods to infer misclassified data. One simulation study is shown regarding how seriously misclassification issue occurs if the number of categories increase. Then, using the group lasso regression, we will show how the best model should be fitted for that kind of multinomial regression problems comprehensively.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 2006년도 추계학술대회
• /
• pp.365-368
• /
• 2006

The implementation of a screening procedure for removing non-conforming products has become a common practice especially in high-tech manufacturing industries. Screening procedures involve a measurement on the quality characteristic of interest since decisions regarding the conformance to specifications are usually made on the basis of the realization of measurement. A significant variability in measurement procedures may result in the misclassification of an outgoing product (that is, falsely accepting defectives or falsely rejecting conforming items), which may lead to wrong interpretation on product quality, It may thus be necessary to consider the impacts of misclassification errors due to measurement variability when designing screening procedures. Along this line, this article investigates the design of screening procedures based on the assessment of misclassification errors. The main objective is to determine the screening limits on measured values so that two types of misclassification errors may properly be compromised.

• 전자공학회논문지B
• /
• 제32B권3호
• /
• pp.522-532
• /
• 1995

Multilayer neural networks with backpropagation learning algorithm are widely used for pattern classification problems. For many real applications, it is more important to reduce the misclassification rate than to increase the rate of successful classification. But multilayer perceptrons(MLP's) have drawbacks of slow learning speed and false convergence to local minima. In this paper, we propose a new method for character recognition problems with a single-layer network and double rejection mechanisms, which guarantees a very low misclassification rate. Comparing to the MLP's, it yields fast learning and requires a simple hardware architecture. We also introduce a new coding scheme to reduce the misclassification rate. We have prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns, and have applied the proposed method for printed character recognition, which shows that the method reduces the misclassification rate significantly without sacrificing the correct recognition rate.

• Journal of the Korean Statistical Society
• /
• 제25권1호
• /
• pp.145-151
• /
• 1996

The local behaviour of the surface formed by the perturbed maximum likelihood estimator of the squared Mahalanobis distance is investigated. The study of the local behaviour allows a simultaneous perturbation on the samples of interest and it is effective in identifying influential observations.

• 응용통계연구
• /
• 제14권1호
• /
• pp.39-57
• /
• 2001

범주형 자료에서 오분류는 자료를 수집하는 과정에서 발생될 수 있다. 오분류되어 있는 자료를 정확한 자료로 간주하여 분석한다면 추정결과에 편의가 발생하고 검정력이 약화되는 결과를 초래하게 되며, 정확하게 분류된 자료를 오분류하고 판단한다면 오분류의 수정을 위해 불필요한 비용과 시간을 낭비해야 할 것이다. 따라서 정확하게 분류된 표본인지 오분류된 표본인지를 판정하는 것은 자료를 분석하기 전에 이루어져야할 매우 중요한 과정이다. 본 논문은 I$\times$J 분할표로 주어지는 범주형 자료에서 두 변수 중 하나의 변수에서만 오분류가 발생되는 경우에 오분류 여부를 검정하기 위해서 오분류 가능성이 없는 변수에 대한 주변합은 고정시키고, 오분류 여부를 가능성이 있는 변수의 주변합을 Sebastiani와 Ramoni(1997)가 제안한 Bound와 외부정보로 표현되는 Collapse의 개념, 그리고 베이지안 방법을 확장하여 자료에 적합한 모형과 사전정보를 고려한 사전모수를 다양하게 설정하면서 재분류하는 연구를 하였다. 오분류에 대한 정보를 얻기 위해서 Tenenbein(1970)에 의해 연구된 이중추출법을 이용하여 오분류 검정을 위한 새로운 통계량을 제안하였으며, 제안된 오분류 검정통계량에 관한 분포를 다양한 모의실험을 통하여 연구하였다.

• 기록학연구
• /
• 제58호
• /
• pp.299-332
• /
• 2018

이 연구는 학교 기록물 분류의 현황을 분석하여 오분류 실태와 그 원인을 밝히고, 이를 토대로 기록관리기준표의 개선방안을 도출하기 위한 것이다. 이를 위해 우선 표본으로 설정된 초, 중, 고 4개 학교가 1년 동안 생산 접수한 기록물을 전수 분석하여 오분류로 의심되는 사례들을 파악하였다. 오분류의 원인 분석과 대안 제시를 위해 행정실장 2명, 공 사립 주무관 2명, 기록연구사 7명 등 총 11명의 자문단을 구성하여 2차에 걸친 집단 면담을 실시하였다. 학교 기록관리기준표에 제시된 공통업무를 중심으로 오분류가 빈번히 이루어지는 33개의 단위과제를 선별하였고, 이들 단위 과제를 중심으로 오분류의 원인을 분석하였다. 오분류의 핵심 원인을 2가지로 유형화하였으며 이러한 원인별로 해설 보강과 복잡한 업무에 대한 업무흐름도 추가라는 기록관리기준표 개선방안을 제시하였다.

• Clinical and Experimental Pediatrics
• /
• 제64권3호
• /
• pp.96-102
• /
• 2021

This study aimed to investigate the effect of maternal smoking during pregnancy (MSDP) on the risk of bone fractures in the offspring through a systematic review and meta-analysis. The PubMed, Web of Science, and Scopus databases were systematically searched for relevant articles published through July 2019. According to heterogeneity, the pooled risk ratio (RR) and odds ratio (OR) and their corresponding 95% confidence interval (CI) were obtained using fixed or random effects models. The heterogeneity and quality of the included studies were assessed by the I-squared (I²) statistic and the Newcastle-Ottawa scale, respectively. Sensitivity analyses were performed to test the effect of MSDP misclassification on the results. The review of 842 search records yielded 5 studies including 8,746 mother-child pairs that were included in the meta-analysis. Pooling adjusted effect measures showed that MSDP was not associated with a later risk of bone fractures in the offspring (pooled RR, 1.15; 95% CI, 0.84-1.58; I²=66.8%; P=0.049). After the adjustment for misclassification, MSDP may be associated with a 27% increased risk of bone fracture (pooled OR, 1.27; 95% CI, 1.00-1.62; I²=0%; P=0.537). After the adjustment for misclassification, MSDP is associated with an increased risk of bone fractures among children whose mothers smoked during pregnancy.