• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.79-96
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• 2013

Exponential distribution plays a key role in engineering reliability and its applications. The exponential failure model has been studied for years. This article introduces two new preliminary test estimators for the reliability function (R(t)) in complete and censored samples from the exponential model with the use of a prior estimation (${\theta}_0$) of the mean (${\theta}$). The proposed preliminary test estimators are studied and compared numerically with the existing estimators. Computer-intensive calculations for bias and relative efficiency show that for, different values of levels of significance and for varying constants involved in the proposed estimators, the proposed estimators are far better than classical and existing estimators.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.619-626
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• 2003

Given a prior guess for the unknown value of P(Y

• International Journal of Reliability and Applications
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• v.16 no.2
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• pp.55-79
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• 2015

It is well known that using any additional information in the estimation of unknown parameters with new sample of observations diminishes the sampling units needed and minimizes the risk of new estimators. There are many rational reasons to assure that the existence of additional information in practice and there exists many practical cases in which additional information is available in the form of target value (initial value) about the unknown parameters. This article is described the problem of how the prior initial value about the unknown parameters can be utilized and combined with classical Bayes estimator to get a new combination of Bayes estimator and prior value to improve the properties of the new combination. In this article, two classes of Bayes-shrinkage and preliminary test Bayes-shrinkage estimators are proposed for the scale parameter of exponential distribution. The bias, risk and risk ratio expressions are derived and studied. The performance of the proposed classes of estimators is studied for different choices of constants engaged in the estimators. The comparisons, conclusions and recommendations are demonstrated.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.152-155
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• 2007

Efficiency and limitations of HAZUS-MH, a GIS based systematic and informative system developed by FEMA and NIBS for natural hazard loss estimations, are discussed by means of a pilot study in the Korean Peninsular. Gyeongsang-do has been selected for the test after careful reviews of previous studies including historical and modern seismicity in the peninsular. A ShakeMap for the selected scenario earthquake with magnitude 6.7 in Gyeongju area is prepared. Then, any losses due to the scenario event have been estimated using HAZUS. Results of the pilot test show that the study area may experience significant physical, economic and social damages. Detailed study in the future will provide efficient and crucial information to the decision makers and emergency agents to mitigate any disaster posed by natural hazards.

• Anatomy and Cell Biology
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• v.56 no.1
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• pp.86-93
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• 2023

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.

• The Journal of Korean Physical Therapy
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• v.19 no.1
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• pp.23-31
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• 2007

Purpose: As a preliminary study, the purpose of this study was to examine the indirect estimation of the lumbar intervertebral disc size by two anthropometric methods, in order to compare these indirect methods with the direct analysis by radiological imaging. Methods: The wrist, elbow, knee, and ankle joint (both right and left) diameters were measured in 52 volunteers, and then intervertebral disc size was calculated using two anthropometric methods (Colombini and modified Colombini). The data were analysed with independent t-test to assess clinical usefulness. Results: When using right and left joint diameter, there was no significant difference in the estimation value of the L4-5 and L5-S1 intervetebral disc sizes. However, the study has shown that male subjects have significantly larger L4-5 and L5-S1 intervertebral disc sizes, compared with those of female subjects. In addition, disc sizes calculated by Colombini's formula were significantly larger than modified Colombini's formula. Conclusion: The indirect estimation of the intervertebral disc size by anthropometric method can be considered as a clinically useful method. However, further study should be conducted to compare anthropometric values with other radiological imaging.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.51.3-52
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• 2020

We present the status of the development of a Markov Chain Monte Carlo (MCMC) parameter estimation (PE) pipeline for compact binary coalescences (CBCs) with the Japanese KAGRA gravitational-wave (GW) detector. The pipeline is included in the KAGRA Algorithm Library (KAGALI). Basic functionalities are benchmarked from the LIGO Algorithm Library (LALSuite) but the KAGRA MCMC PE pipeline will provide a simpler, memory-efficient pipeline to estimate physical parameters from gravitational waves emitted from compact binaries consisting of black holes or neutron stars. Applying inspiral-merge-ringdown and inspiral waveforms, we performed simulations of various black hole binaries, we performed the code sanity check and performance test. In this talk, we present the situation of GW observation with the Covid-19 pandemic. In addition to preliminary PE results with the KAGALI MCMC PE pipeline, we discuss how we can optimize a CBC PE pipeline toward the next observation run.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.595-628
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• 2011

This study investigates the issues and suggests reform measures in applying CV methods to the Korea Development Institute's (KDI's) Preliminary Feasibility Test (PFT) of public projects. Most public projects on culture, science and environment evaluated under the PFT system belong to the category of "nonstandard" projects whose outputs are non-marketed, and CV is currently the main tool used for their benefit estimation. A careful discussion and investigation is recommended for the selection of target population, payment vehicle, and number of payment times. Operating expert reviews, focus group interviews, and pre-tests is highly recommended to reduce the potential bias involved in the CV studies. A single or double bounded dichotomous choice format is the most popular design of questionnaire, but we identify several undissolved issues in designing and implementing the format. Some other forms of inducing WTPs may still deserve our consideration. Various specifications of the WTP function need to be tried and tested based on their stability, in particular. Employing a nonparametric approach is also recommended. Treatments of 0 or negative WTPs and protest bids are shown to be the most serious issues that affect the estimation results significantly. We review diverse measures of handling those issues and summarize their advantages and shortcomings.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.