• Title/Summary/Keyword: linear probability model

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Bayesian Variable Selection in Linear Regression Models with Inequality Constraints on the Coefficients (제한조건이 있는 선형회귀 모형에서의 베이지안 변수선택)

  • 오만숙
    • The Korean Journal of Applied Statistics
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    • v.15 no.1
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    • pp.73-84
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    • 2002
  • Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

Development and Comparison of Data Mining-based Prediction Models of Building Fire Probability

  • Hong, Sung-gwan;Jeong, Seung Ryul
    • Journal of Internet Computing and Services
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    • v.19 no.6
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    • pp.101-112
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    • 2018
  • A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

Wedge Failure Probability Analysis for Rock Slope Based on Non-linear Shear Strength of Discontinuity (불연속면의 비선형 전단강도를 이용한 암반사면 쐐기파괴 확률 해석)

  • 윤우현;천병식
    • Journal of the Korean Geotechnical Society
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    • v.19 no.6
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    • pp.151-160
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    • 2003
  • The stability of the designed rock slope is analysed based on two kinds of shear strength model. Besides the deterministic analysis, a probabilistic approach on Monte Carlo simulation is proposed to deal with the uncertain characteristics of the discontinuity and the results obtained from two models are compared to each other. To carry out the research of characteristics of the discontinuity, BIPS, DOM Scanline survey data and direct shear test data are used, and chi-square test is used for determining the probability distribution function. The rock slope is evaluated to be stable in the deterministic analysis, but in the probabilistic analysis, the probability of failure is more than 5%, so, it is considered that the rock slope is unstable. In the shear strength models, the probability of the failure based on the Mohr-Coulomb model(linear model) is higher than that of the Barton model. It is supported by the fact that the Mohr-Coulomb model is more sensitive to block size than the Barton model. In fact, there is no reliable way to estimate the unit cohesion of the Mohr-Coulomb model except f3r back analysis and in the case of small block failure in the slope, Mohr-Coulomb model may excessively evaluate the factor of the safety. So, the Barton model of which parameters are easily acquired using the geological survey is more reasonable for the stability of the studied slope. Also, the selection of the proper shear strength model is an important factor for slope failure analysis.

Uncertainty Analysis of Parameters of Spatial Statistical Model Using Bayesian Method for Estimating Spatial Distribution of Probability Rainfall (확률강우량의 공간분포추정에 있어서 Bayesian 기법을 이용한 공간통계모델의 매개변수 불확실성 해석)

  • Seo, Young-Min;Park, Ki-Bum;Kim, Sung-Won
    • Journal of Environmental Science International
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    • v.20 no.12
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    • pp.1541-1551
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    • 2011
  • This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

A Note on the Asymptotic Property of S2 in Linear Regression Model with Correlated Errors

  • Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.233-237
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    • 2003
  • An asymptotic property of the ordinary least squares estimator of the disturbance variance is considered in the regression model with correlated errors. It is shown that the convergence in probability of S$^2$ is equivalent to the asymptotic unbiasedness. Beyond the assumption on the design matrix or the variance-covariance matrix of disturbances error, the result is quite general and simplify the earlier results.

Bayesian Outlier Detection in Regression Model

  • Younshik Chung;Kim, Hyungsoon
    • Journal of the Korean Statistical Society
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    • v.28 no.3
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    • pp.311-324
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    • 1999
  • The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

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A Bayesian Approach to Detecting Outliers Using Variance-Inflation Model

  • Lee, Sangjeen;Chung, Younshik
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.805-814
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    • 2001
  • The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for outliers problem and also analyze it in linear regression model using a Bayesian approach with the variance-inflation model. We will use Geweke's(1996) ideas which is based on the data augmentation method for detecting outliers in linear regression model. The advantage of the proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability The sampling based approach can be used to allow the complicated Bayesian computation. Finally, our proposed methodology is applied to a simulated and a real data.

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A Study on Dynamic Modeling of Photovoltaic Power Generator Systems using Probability and Statistics Theories (확률 및 통계이론 기반 태양광 발전 시스템의 동적 모델링에 관한 연구)

  • Cho, Hyun-Cheol
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.7
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    • pp.1007-1013
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    • 2012
  • Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

A Study on the Inference Model of In-use Vehicles Emission Distribution according to the Vehicle Mileage (주행거리별 운행차 배출가스 분포 추정 모델에 관한 연구)

  • 김현우
    • Transactions of the Korean Society of Automotive Engineers
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    • v.10 no.4
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    • pp.85-92
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    • 2002
  • To investigate the safety of the in-use vehicles emission against the tail-pipe emission regulation, in-use vehicles emission trend according to vehicle mileage should be known. But it is impossible to collect all vehicles emission data In order to know that. Therefore, it is necessary to establish a statistically meaningful inference method that can be used generally to estimate in-use vehicles emissions distribution according to the vehicle mileage with relatively less in-use vehicles emission data. To do this, a linear regression model that solved the problems of data normality and common variance of error was studied. As a way that can secure the data normality, In(emission) instead of emission itself was used as a sampled data. And a reciprocal of mileage was suggested as a factor to secure common variance of error. As an example, 36 data of FTP-75 test were handled in this study. As a result, using average value and standard deviation at each mileage which were inferred from a linear regression model, probability density distribution and cumulative distribution of emissions according to the vehicle mileage were obtained and it was possible to predict the deterioration factor through full useful life mileage and also possible to decide whether those in-use vehicles will meet the tail-pipe emission regulations or not.

Stochastic Probability Model for Preventive Management of Armor Units of Rubble-Mound Breakwaters (경사제 피복재의 유지관리를 위한 추계학적 확률모형)

  • Lee, Cheol-Eung;Kim, Sang Ug
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.33 no.3
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    • pp.1007-1015
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    • 2013
  • A stochastic probability model based on the non-homogeneous Poisson process is represented that can correctly analyze the time-dependent linear and nonlinear behaviors of total damage over the occurrence process of loads. Introducing several types of damage intensity functions, the probability of failure and the total damage with respect to mean time to failure has been investigated in detail. Taking particularly the limit state to be the random variables followed with a distribution function, the uncertainty of that would be taken into consideration in this paper. In addition, the stochastic probability model has been straightforwardly applied to the rubble-mound breakwaters with the definition of damage level about the erosion of armor units. The probability of failure and the nonlinear total damage with respect to mean time to failure has been analyzed with the damage intensity functions for armor units estimated by fitting the expected total damage to the experimental datum. Based on the present results from the stochastic probability model, the preventive management for the armor units of the rubble-mound breakwaters would be suggested to make a decision on the repairing time and the minimum amounts repaired quantitatively.