• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.145-151
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• 2004

PROC MIXED fits a variety of mixed models to data and enables one to use these fitted models to make statistical inferences about the data. However, the simulation study in this article shows that PROC MIXED using REML estimators provides one with a confidence interval, that does not keep the stated confidence coefficients, on sums of two variance components in the simple regression model with unbalanced nested error structure which is a mixed model.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1169-1175
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• 2008

The validation technique is classified with two methods whether to demand of additional experimental points. The method which requires additional experimental points such as RSME is actually impossible in engineering field. Therefore, the method which only use experimented points such as the cross validation technique is only available. But the cross validation not only requires considerable computational costs for generating metamodel each iterations, but also cannot measure quantitatively the fidelity of metamodel. In this research we propose a new validation technique for representative metamodels using an variance of metamodel and confidence interval information. The proposed validation technique computes confidence intervals using a variance information from the metamodel. This technique will have influence on choosing the accurate metamodel, constructing ensemble of each metamodels and advancing effectively sequential sampling technique.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.257-266
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• 2007

It is known that Agresti-Coull approach is an effective tool for the construction of confidence intervals for various problems related to binomial proportions. However, the Agrest-Coull approach often produces a conservative confidence interval. In this note, confidence intervals based on a Bayesian approach are proposed for a linear function of independent binomial proportions. It is shown that the Bayesian confidence interval slightly outperforms the confidence interval based on Agresti-Coull approach in average sense.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.93-104
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• 1996

In this paper, Marshall and Olkin's bivariate exponential distribution is assumed for stress and strength model. We derive the asymptotic distributions and construct some approximate confidence intervals for P(X

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.311-315
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• 1997

In this note we consider confidence interval based on Kolmogorov-Smirnov statistic. In order to obtain confidence interval we need percentage points of the statistics. Bootstrap method is examined whether it is useful to determine the points. It is concluded that the method is useful for observations with many ties, whereas it gives less conserbative points for continuous distributions.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1109-1115
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• 2010

In this paper, exact confidence interval of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the performance of exact confidence interval estimates of hypergeometric parameter in terms of actual coverage probability by small sample Monte Carlo simulation.

• Journal of Educational Research in Mathematics
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• v.24 no.3
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• pp.375-385
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• 2014

This study compares the confidence interval estimation of population mean with that of population ratio, and considers whether these two estimations ensures consistency. As a result, this study suggests the following acquisition method of consistency : dealing with population mean and population ratio in the same mode, substituting the observed or experimental value of sample standard deviation for standard deviation in population in setting a confidence interval of both population mean and population ratio, and distinguishing population ratio $\hat{P}$ from its observed vale $\hat{p}$.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.87-100
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• 2005

In order to construct confidence intervals on the sum of variance components in a simple regression model with unbalanced nested error structure, alternative confidence intervals using Graybill and Wang(1980) and generalized inference concept introduced by Tsui and Weerahandi(1989) are proposed. Computer simulation programmed by SAS/IML is performed to compare the simulated confidence coefficients and average interval lengths of the proposed confidence intervals. A numerical example is provided to demonstrate the confidence intervals and to show consistency between the example and simulation results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.708-715
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• 2014

In this paper, we suggest a method of realtime confidence interval estimation to detect abnormal states of sensor data. For realtime confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, where compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarming. As the suggested method is for realtime anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through realtime confidence interval estimation.

• Journal of muscle and joint health
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• v.29 no.1
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• pp.41-49
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• 2022

Purpose: The purpose of this study was to assess the influence of nurses staffing level on patient health outcomes in intensive care units (ICUs) in Korea. Methods: The study was retrospective in nature. Information on patients and their outcomes, as well as nurse cohort data, were obtained from Korea's National Health Insurance Service Database. The observation period was from January 1, 2008 to December 31, 2018, and data for 2,964,991 patients were analyzed. Independent variables included patient' age and sex and hospital type, intensivist, and nurses staffing level. Results: The mortality rate in ICUs was significantly higher at tertiary hospitals with a level 3~4 (HR, 1.21; 95% confidence interval, 1.19~1.22) or level 5~9 nurse staffing (HR, 1.31; 95% confidence interval, 1.27~1.34) compare to that of tertiary hospitals with a 1~2 level. 28-day mortality rate was also higher at general hospitals with a level 3~4 (HR, 1.13; 95% confidence interval, 1.12~1.14), level 5~6 (HR. 1.34; 95% confidence interval, 1.32~1.36), level 7~9 nurse staffing (HR, 1.38; 95% confidence interval, 1.38~1.42), using level 1~2 as reference. Conclusion: Nurses staffing level is a key determinant of healthcare-associated mortality in critically ICUs patients. Policies to achieve adequate nurse staffing levels are therefore required to enhance patient outcomes.