• Biotechnology and Bioprocess Engineering:BBE
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• 제11권1호
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• pp.67-71
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• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.445-461
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• 2019

It is possible that data are not always fitted with sufficient precision by the existing distributions; therefore this article presents a methodology that enables the use of families of asymmetric distributions as alternative probabilistic models for survival analysis, with censorship on the right, different from those usually studied (the Exponential, Gamma, Weibull, and Lognormal distributions). We use a more flexible parametric model in terms of density behavior, assuming that data can be fit by a distribution of stable distribution families considered unconventional in the analyses of survival data that are appropriate when extreme values occur, with small probabilities that should not be ignored. In the methodology, the determination of the analytical expression of the risk function h(t) of the $L{\acute{e}}vy$ distribution is included, as it is not usually reported in the literature. A simulation was conducted to evaluate the performance of the candidate distribution when modeling survival times, including the estimation of parameters via the maximum likelihood method, survival function ${\hat{S}}$(t) and Kaplan-Meier estimator. The obtained estimates did not exhibit significant changes for different sample sizes and censorship fractions in the sample. To illustrate the usefulness of the proposed methodology, an application with real data, regarding the survival times of patients with colon cancer, was considered.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회
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• pp.416-422
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• 2010

Estimating condition of geotechnical structures are difficult because of nonlinear time dependency and seasonal effects. Measuring data of structure failure is highly variable in time and space, and a unique approach cannot be defined to model structure movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, this method is advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured.

• 응용통계연구
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• 제22권3호
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• pp.607-616
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• 2009

In many epidemiological studies, the occurrence times of the event of interest are right-censored or interval censored. In certain situations such as the AIDS data, however, the incubation period which is the time between HIV infection and the diagnosis of AIDS is usually doubly censored. In this paper, we impute the interval censored HIV infection time using three imputation methods. Mid imputation, conditional mean imputation and approximate Bayesian bootstrap are implemented to obtain right censored data, and then Gibbs sampler is used to estimate the coefficient factor of the incubation period. By using Bayesian approach, flexible modeling and the use of prior information is available. We applied both parametric and semi-parametric methods for estimating the effect of the covariate and compared the imputation results incorporating prior information for the covariate effects.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.50-54
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• 2004

A numerical system to extract effective elastic properties of polycrystalline thin-films for MEMS devices is already developed. In this system, the statistical model based on lattice system is used for modeling the microstructure evolution simulation and the key kinetics parameters of given micrograph, grain distributions and deposition process can be extracted by inverse method proposed in the system. In this work, the effects of kinetics parameters on the extraction of effective elastic properties of polycrystalline thin-films are studied by using statistical method. The effects of the fraction of the potential site( $f_{P}$ ) and the nucleation probability( $P_{N}$ ) among the parameters for deposition process of microstructure on the extraction of effective elastic properties of polycrystalline thin-films are studied.d.d.

• 전기학회논문지
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• 제63권2호
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• pp.291-296
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• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of weak stationarity. The purpose of this study is to analyze the accuracy of the Run-test, modified Run-test, RA(reverse arrangement)-test, and modified RA-test for assessing surface EMG signal stationarity. Six stationary and three non-stationary signals were simulated by using sine wave, AR(autoregressive) modeling, and real surface EMG. The simulated signals were tested for stationarity using nine different methods of Run-test and RA-test. The results showed that the modified Run-test method2 (mRT2) classified exactly the surface EMG signals by stationarity with 100% accuracy. This finding indicates that the mRT2 may be the best way for assessing stationarity in surface EMG signals.

• 한국안전학회지
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• 제32권5호
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• pp.115-121
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• 2017

This paper is to introduce the main modeling assumptions and data structures associated with right-censored data to describe the successful methodological ideas for analyzing such a field-failure-data when components operating in different environments. The Kaplan - Meier method is the most popular method used for survival analysis. Together with the log-rank test, it may provide us with an opportunity to estimate survival probabilities and to compare survival between groups. An important advantage of the Kaplan - Meier curve is that the method can take into account some types of censored data, particularly right-censoring. The above non-parametric method was used to verify the equality of parts life used in different environments. After that, we performed the life distribution analysis using the parametric method. We simulated data from three distributions: exponential, normal, and Weibull. This allowed us to compare the results of the estimates to the known true values and to quantify the reliability indices. Here we used the Akaike information criterion to find a suitable life time distribution. If the Akaike information criterion is the smallest, the best model of failure data is presented. In this paper, no-nparametrics and parametrics methods are analyzed using R program which is a popular statistical program.

• Structural Engineering and Mechanics
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• 제89권1호
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• pp.61-75
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• 2024

Strengthening of hollow structural sections using through-bolts is a cost-effective and straightforward approach. It's a versatile method that can be applied during both design and service phases, serving as a non-disruptive and budget-friendly retrofitting solution. Existing research on axially loaded hollow sections T-joints has demonstrated that this technique can amplify the joint strength by 50%, where single bolt could enhance the strength of the joint by 35%. However, there's a gap in understanding their use for K-joints. As the behavior of K-joints is more complex, and they are widely existent in structures, this study aims to bridge that gap by conducting comprehensive parametric study using finite element analysis. Numerical investigation was conducted to evaluate the effect of through bolts on K-joints focusing on using single through bolt to achieve most of the strengthening effect. A full-scale parametric model was developed to investigate the effect of various geometric parameters of the joint. This study concluded the existence of optimal bolt location to achieve the highest strength gain for the joint. Moreover, a rigorous statistical analysis was conducted on the data to propose design equations to predict optimal bolt location and the corresponding strength gain implementing the verified by finite element models.

• 한국전산구조공학회논문집
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• 제32권1호
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• pp.55-63
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• 2019

공학문제에서 많은 확률 변수들은 상관성을 가지고 있고, 입력변수의 상관성은 기계시스템의 통계적 성능 분석 결과에 큰 영향을 미친다. 하지만, 상관 변수들은 결합분포함수를 모델링하기 어렵다는 이유로 종종 독립변수로 취급되거나 특정한 모수적 모델로 표현되는 경우가 많으며, 특히 데이터가 적은 경우 결합분포함수를 정확히 모델링하는데 더 큰 어려움이 있다. 본 연구에서 개발된 경계데이터를 이용한 다변량 커널밀도추정은 비선형성을 갖는 다양한 형태의 다변량 확률 분포 추정을 위해 개발되었다. 다변량 커널밀도추정은 주어진 데이터와 균등분포함수의 파라미터의 신뢰구간으로부터 생성된 경계데이터를 결합하여 데이터의 질과 수에 덜 민감하다. 따라서 제안된 방법은 보수적인 통계모델링과 신뢰성 해석 결과를 도출할 수 있으며, 통계시뮬레이션과 공학예제를 통해 그 성능을 검증하였다.