• Journal of Preventive Medicine and Public Health
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• v.31 no.4 s.63
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• pp.875-884
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• 1998

Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the EM algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which uses only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.610-619
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• 2007

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.53-62
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• 2010

This paper introduces a classification algorithm which can be applied to a learning problem with incomplete data sets, missing variable values or a class value. This algorithm uses a data expansion method which utilizes weighted values and probability techniques. It operates by extending a classifier which are considered to be in the optimal projection plane based on Fisher's formula. To do this, some equations are derived from the procedure to be applied to the data expansion. To evaluate the performance of the proposed algorithm, results of different measurements are iteratively compared by choosing one variable in the data set and then modifying the rate of missing and non-missing values in this selected variable. And objective evaluation of data sets can be achieved by comparing, the result of a data set with non-missing variable with that of C4.5 which is a known knowledge acquisition tool in machine learning.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.149-157
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• 2011

Undecided inference could be regarded as a missing data problem such as MARand MNAR. Under the assumption of MAR, undecided inference make use of logistic regression model. The probability of default for the undecided group is obtained with regression coefficient vectors for the decided group and compare with the probability of default for the decided group. And under the assumption of MNAR, undecide dinference make use of logistic regression model with additional feature random vector. Simulation results based on two kinds of real data are obtained and compared. It is found that the misclassification rates are not much different from the rate of rawdata under the assumption of MAR. However the misclassification rates under the assumption of MNAR are less than those under the assumption of MAR, and as the ratio of the undecided group is increasing, the misclassification rates is decreasing.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.89-92
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• 2000

The wireless network has bursty and high error rates. Due to the quite limited bandwidth in wireless networks, images are usually transmitted as a compressed version with VLC(variable length coding). Loss of coded data can affect a decoded image to a large extent, making concealment of errors caused by data loss an important issue. This paper presents a error concealment technique for DCT(Discrete Cosine Transform) based image coding. First, a method to estimate the missing DC coefficients of a JPEG coded image which is required for decoding the compressed image, is suggested and evaluated. Second, the missing data is interpolated by exploiting the probability of being nonzero and the correlation between adjacent blocks. In addition, since the these technique is computational efficient, it conserves system resources and power consumption, which are restrictive in mobile computers.

• Journal of Dental Anesthesia and Pain Medicine
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• v.18 no.2
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• pp.97-103
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• 2018

Background: Respiration monitoring is necessary during sedation for dental treatment. Recently, acoustic respiration rate ($RRa^{TM}$), an acoustics-based respiration monitoring method, has been used in addition to auscultation or capnography. The accuracy of this method may be compromised in an environment with excessive noise. This study evaluated whether noise from the ultrasonic scaler affects the performance of RRa in respiratory rate measurement. Methods: We analyzed data from 49 volunteers who underwent scaling under intravenous sedation. Clinical tests were divided into preparation, sedation, and scaling periods; respiratory rate was measured at 2-s intervals for 3 min in each period. Missing values ratios of the RRa during each period were measuerd; correlation analysis and Bland-Altman analysis were performed on respiratory rates measured by RRa and capnogram. Results: Respective missing values ratio from RRa were 5.62%, 8.03%, and 23.95% in the preparation, sedation, and scaling periods, indicating an increased missing values ratio in the scaling period (P < 0.001). Correlation coefficients of the respiratory rate, measured with two different methods, were 0.692, 0.677, and 0.562 in each respective period. Mean capnography-RRa biases in Bland-Altman analyses were -0.03, -0.27, and -0.61 in each respective period (P < 0.001); limits of agreement were -4.84-4.45, -4.89-4.15, and -6.18-4.95 (P < 0.001). Conclusions: The probability of missing respiratory rate values was higher during scaling when RRa was used for measurement. Therefore, the use of RRa alone for respiration monitoring during ultrasonic scaling may not be safe.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.485-492
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• 2014

Spectrum sensing for cognitive-radio applications may use a matched-filter detector (in the presence of full knowledge of the signal that may be transmitted by the primary user) or an energy detector (when that knowledge is missing). An intermediate situation occurs when the primary signal is imperfectly known, in which case we advocate the use of a linear-quadratic detector. We show how this detector can be designed by maximizing its deflection, and, using moment-bound theory, we examine its robustness to the variations of the actual probability distribution of the inaccurately known primary signal.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.593-600
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• 2007

Under repeated surveys, missing values often appear for various reasons and are replaced by new samples. It is investigated that the existing estimator in repeated survey by Jessen (1942), which has been originally developed for the new samples of fixed size, can be used in such situation where the size of new samples is random. It is shown that the proposed estimator has smaller variance than the sample mean.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.6-8
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• 2009

In this paper. we propose receiver algorithms for VHF(Very High Frequency) digital link mode-2(VDL Mode-2) systems. Unlike conventional digital communication systems using the root raised cosine filter as a transmit and receive filter, raised cosine filter is used as a transmit filter in VDL Mode-2 systems. Hence, it is crucial to design and implement the optimum lowpass receive filter by considering the amount of inter-symbol interference and noise performance. On the other hand, due to the short preamble pattern, it is crucial to develop an efficient packet detection algorithm for reliable communication link. In this paper, we design the optimum receive filter and packet detection algorithm and evaluate the performance of receiver adopting the proposed receive filter and packet detection algorithm.