• Journal of Applied Reliability
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• v.2 no.2
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• pp.121-133
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• 2002

This paper concerns informative censoring and some of the difficulties it creates in analysis of survival data. For analyzing censored data, misclassification of informative censoring into random censoring is often unavoidable. It is worthwhile to investigate the impact of neglecting informative censoring on the estimation of the parameters of the proportional hazards model. The proposed model includes a primary failure which can be censored informatively or randomly and a followup failure which may be censored randomly. Simulation shows that the loss is about 30% with regard to the confidence interval if we neglect the informative censoring.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.77-82
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• 2019

The purpose of this study is to analyze the relationship between the controlling shareholding ratio and the business diversification of savings banks The difference in this study is the analysis of the relationship between the controlling shareholding ratio of the savings bank and the business diversification using panel data. In this study, the semi-annual financial statements for the period 2014-2018 were used on the basis of a sample of 79 saving banks. The research model is analyzed using random effects generalized linear square (GLS) model considering the autocorrelation problem. As a result of the empirical analysis, it is estimated that the relationship between the controlling shareholding ratio of the savings bank and the business diversification is significant (+). This is the result of supporting the hedging hypothesis.

• Journal of Ocean Engineering and Technology
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• v.18 no.4 s.59
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• pp.23-32
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• 2004

Specially shaped concrete blocks are used for the armor layer of rubble structure for breakers, seawalls, or other shore protection work. In this study, the hydraulic characteristics of the Modified-Tribar(MTB), which addresses the shortcomings of the Arch-Tribar, and the most widely used Tetrapod(TTP) in Korea are examined through hydraulic model tests. The MTB are much more stable than the TTP, as shown through the stability model tests under non-breaking and non-overtopping condition. The value of the stability coefficient(KD) was obtained at around 30. The model tests show that the TTP random two layers and MTB uniform 1.5 layers have similar effects, but the MTB one layer shows slightly low effects in dissipating wave energy. The TTP random two layer model is the most effective in reducing wave overtopping rate, under overtopping condition, while the MTB uniform one layer and the MTB uniform 1.5 layer models follow respectively.

• Journal of Mechanical Science and Technology
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• v.19 no.6
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• pp.1280-1289
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• 2005

This paper presents the effects of corrosion environments of failure pressure model for buried pipelines on failure prediction by using a failure probability. The FORM (first order reliability method) is used in order to estimate the failure probability in the buried pipelines with corrosion defects. The effects of varying distribution types of random variables such as normal, lognormal and Weibull distributions on the failure probability of buried pipelines are systematically investigated. It is found that the failure probability for the MB31G model is larger than that for the B31G model. And the failure probability is estimated as the largest for the Weibull distribution and the smallest for the normal distribution. The effect of data scattering in corrosion environments on failure probability is also investigated and it is recognized that the scattering of wall thickness and yield strength of pipeline affects the failure probability significantly. The normalized margin is defined and estimated. Furthermore, the normalized margin is used to predict the failure probability using the fitting lines between failure probability and normalized margin.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.969-976
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• 2008

Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.5
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• pp.424-433
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• 1991

In the noncoherent FH/FSK system presence of the multitione jamming and noise, in the case random and 송 structures jamming model, the performance analyzed that random and struetured jamming derived error proability. It is found that error probability and performance when error corecting code used Hamming. BCH, Convolutional code under the worst case partial band jamming interference.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.489-501
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• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.

• Structural Engineering and Mechanics
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• v.63 no.5
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• pp.659-667
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• 2017

Rail support failure is inevitably subjected to track geometric deformations. Due to the randomness and evolvements of track irregularities, it is naturally a hard work to grasp the trajectories of dynamic responses of railway systems. This work studies the influence of rail fastener failure on dynamic behaviours of wheel/rail interactions and the railway tracks by jointly considering the effects of track random irregularities. The failure of rail fastener is simulated by setting the stiffness and damping of rail fasteners to be zeroes in the compiled vehicle-track coupled model. While track random irregularities will be transformed from the PSD functions using a developed probabilistic method. The novelty of this work lays on providing a method to completely reveal the possible responses of railway systems under jointly excitation of track random irregularities and rail support failure. The numerical results show that rail fastener failure has a great influence on both the wheel/rail interactions and the track vibrations if the number of rail fastener failure is over three. Besides, the full views of time-dependent amplitudes and probabilities of dynamic indices can be clearly presented against different failing status.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.755-762
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• 2015

One of the main objectives of the U.S. Census Bureau is the proper estimation of median household income for small areas. These estimates have an important role in the formulation of various governmental decisions and policies. Since direct survey estimates are available annually for each state or county, it is desirable to exploit the longitudinal trend in income observations in the estimation procedure. In this study, we consider Fay-Herriot type small area models which include time-specific random effect to accommodate any unspecified time varying income pattern. Analysis is carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. We have evaluated our estimates by comparing those with the corresponding census estimates of 1999 using some commonly used comparison measures. It turns out that among three types of time-specific random effects the small area model with a time series random walk component provides estimates which are superior to both direct estimates and the Census Bureau estimates.