• Journal of the Korean Statistical Society
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• v.21 no.2
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• pp.139-151
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• 1992

Simultaneous confidence intervals for the parameters in the logistic regression models with random regressors are considered. A method based on the bootstrap and its stochastic approximation will be developed. A key idea in using the bootstrap method to construct simultaneous confidence intervals is the concept of prepivoting which uses the transformation of a root by its estimated cumulative distribution function. Repeated use of prepivoting makes the overall coverage probability asymptotically correct and the coverage probabilities of the individual confidence statement asymptotically equal. This method is compared with ordinary asymptotic methods based on Scheffe's and Bonferroni's through Monte Carlo simulation.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.311-314
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• 2003

Concrete structures such as bridge, pavement, airfield, and offshore structure are normally subjected to repeated load. This paper proposes a failure probability models of concrete subjected to split tension repeated-loads, based on experimental results. The fatigue tests were performed at the stress ratio of 0.1, the loading shape of sine, the frequency of 20Hz, and the stress levels of 90, 80 and 70%. The fatigue test specimen was 150mm in diameter and 75mm in thickness. The fatigue analysis did not include which exceeded 0.9 of statistical coefficient of determination values or did not failure at 2$\times$$10^6$ cycles. The graphical method, the moment method, and maximum likelihood estimation method were used to obtain Weibull distribution parameters. The goodness-of-fit test by Kolmogorov-Smirnov test was acceptable 5% level of significance. As a result, the proposed failure probability model based on the two-parameter($\alpha and \mu$) Weibull distribution was good enough to estimate accurately the fatigue life subjected to tension mode.

• Journal of Korean Society for Quality Management
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• v.19 no.2
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• pp.145-157
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• 1991

Thid study intends to pursue behavioral-structure of production behavior through statistical models which are using in PERT and Queueing theory. We can corprehand the orders of human production behavior's characteristics by several related attributes of probablity/statistics. These attributes are poisson, Beta, exponential distributions and P.S Laplace's natural probability. Human production behavior is related and regressed to these attributes in many divisions intermediately. Progressive numerical understanding in many essential human behavior acts on the application of practical behavior standard in production word and operation.

• Journal of the Korean Statistical Society
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• v.14 no.2
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• pp.87-94
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• 1985

This paper extends the results of Ellis and Rosen (1982 a) to some more general integrals and applies our main theorem to compute the specific free energy of some models in statistical mechanics. The general integrals of this paper mean the integrals with respect to the probability measures induced by the sample mean of n i.i.d. random variables taking values in a separable Banach space.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.811-827
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• 2010

This paper deals with a density estimation method in binary choice models that can be regarded as a statistical inverse problem. We use an orthogonal basis to estimate density function and consider the choice of an appropriate truncation parameter to reflect the model complexity and the prediction accuracy. We propose a data-dependent rule to choose the truncation parameter in the context of binary choice models. A numerical simulation is provided to illustrate the performance of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.11-19
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• 1994

The matching probability P(ο/$\lambda$), of the signal sequence(ο) observed for a finite time interval with a HMM (Hidden Markov Model $\lambda$) indicates the probability that signal comes from the given model. By utilizing the fact that the probability represents matching score of the observed signal with the model we can recognize an unknown signal pattern by comparing the magnitudes of the matching probabilities with respect to the known models. Because the algorithm however uses floating point variables during the computing process hardware implementation of the algorithm requires floating point units. This paper proposes an integer algorithm which uses positive integer numbers rather than float point ones to compute the matching probability so that we can economically realize the algorithm into hardware. The algorithm makes the model parameters integer numbers by multiplying positive constants and prevents from divergence of data through the normalization of variables at each step. The final equation of matching probability is composed of constant terms and a variable term which contains logarithm operations. A scheme to make the log conversion table smaller is also presented. To analyze the qualitive characteristics of the proposed algorithm we attatch simulation result performed on two groups of 10 hypothetic models respectively and inspect the statistical properties with repect to the model order the magnitude of scaling constants and the effect of the observation length.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.441-457
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• 2024

Semicontinuous data are characterized by a mixture of a point probability mass at zero and a continuous distribution of positive values. This type of data is often modeled using a two-part model where the first part models the probability of dichotomous outcomes -zero or positive- and the second part models the distribution of positive values. Despite the two-part model's popularity, variable selection in this model has not been fully addressed, especially, in high dimensional data. The objective of this study is to investigate variable selection and prediction performance of penalized regression methods in two-part models. The performance of the selected techniques in the two-part model is evaluated via simulation studies. Our findings show that LASSO and ENET tend to select more predictors in the model than SCAD and MCP. Consequently, MCP and SCAD outperform LASSO and ENET for β-specificity, and LASSO and ENET perform better than MCP and SCAD with respect to the mean squared error. We find similar results when applying the penalized regression methods to the prediction of crime incidents using community-based data.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal of Industrial Technology
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• v.31 no.B
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• pp.107-112
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• 2011

Recently, climate change around the world due to global warming has became an important issue and damages by climate change have a bad effect on human life. Changes of Sea Surface Temperature(SST) is associated with natural disaster such as Typhoon and El Nino. So we predicted daily future SST using Statistical Downscaling Method and CGCM 3.1 A1B scenario. 9 points of around Korea peninsular were selected to predict future SST and built up a regression model using Multiple Linear Regression. CGCM 3.1 was simulated with regression model, and that comparing Probability Density Function, Box-Plot, and statistical data to evaluate suitability of regression models, it was validated that regression models were built up properly.