• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.991-1002
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• 2014

Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.343-352
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• 2010

Recently, many researchers have been carried out to estimate more controlled service life and long-term performance of carbonated concrete structures. Durability analysis and design based on probability have been induced to new concrete structures for design. This paper provides a carbonation prediction model based on the Fick's 1st law of diffusion using statistic data of carbonated concrete structures and the probabilistic analysis of the durability performance has been carried out by using a Bayes' theorem. The influence of concerned design parameters such as $CO_2$ diffusion coefficient, atmospheric $CO_2$ concentration, absorption quantity of $CO_2$ and the degree of hydration was investigated. Using a monitoring data, this model which was based on probabilistic approach was predicted a carbonation depth and a remaining service life at a variety of environmental concrete structures. Form the result, the application method using a realistic carbonation prediction model can be to estimate erosion-open-time, controlled durability and to determine a making decision for suitable repair and maintenance of carbonated concrete structures.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.737-746
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• 2013

The Bayesian method can be applied successfully to the estimation of the censored regression model introduced by Tobin (1958). The Bayes estimates show improvements over the maximum likelihood estimate; however, the performance of the Bayesian interval estimation is questionable. In Bayesian paradigm, the prior distribution usually reflects personal beliefs about the parameters. Such subjective priors will typically yield interval estimators with poor frequentist properties; however, an objective noninformative often yields a Bayesian procedure with good frequentist properties. We examine the performance of frequentist properties of noninformative priors for the Tobit regression model.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.27-30
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• 2012

One of the most fundamental functions required for an intelligent agent is to estimate its current position based upon uncertain sensor data. In this paper, we explain the implementation of a robot localization system using Particle filters, which are the most effective one of the probabilistic localization methods, and then present the result of experiments for evaluating the performance of our system. Through conducting experiments to compare the effect of the noise-free model with that of the noisy state transition model considering inherent errors of robot actions, we show that it can help improve the performance of the Particle filter localization to apply a state transition model closely approximating the uncertainty of real robot actions.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.21-30
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level, and based on the time the performances of items are investigated at the normal stress level. In this paper, when the mean of the prior of a failure rate is known in the exponential lifetime distribution with censored accelerated failure time data, we utilize the empirical Bayesian method by using the moment estimators in order to estimate the parameters of the prior distribution and obtain the empirical Bayesian predictive density and predictive intervals for a future observation under the normal stress level.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• Safety and Health at Work
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• v.8 no.2
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• pp.206-211
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• 2017

Background: Self-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor-response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried. Methods: This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function. Results: Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found. Conclusion: A risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.31-40
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• 2009

Spatial time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations. In this paper, We estimate the parameters of spatial time autoregressive moving average (SIARMA) process by method of Gibbs sampling. Finally, We apply this method to a set of U.S. Mumps data over a 12 states region.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.755-762
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• 2013

In this paper we consider the NHPP software reliability model. And we deal with the maximum likelihood estimation and the Bayesian estimation with conjugate prior for parameter inference in the mean value function of Goel-Okumoto model (1979). The parameter estimates for the proposed model is presented by MLE and Bayes estimator in data set. We compare the predicted number of faults with the actual data set using the proposed mean value function.