• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1419-1427
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• 2008

This paper shows the kernel Poisson regression which can be applied in the claims reserving, where the row effect is assumed to be a nonlinear function of the row index. The paper concentrates on the chain-ladder technique, within the framework of the chain-ladder linear model. It is shown that the proposed method can provide better reserve estimates than the Poisson model. The cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• Ocean and Polar Research
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• v.26 no.3
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• pp.465-473
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• 2004

Five parameters such as the entrance size of the front wall, conduit size, wave period, wave height and the width of water pool were selected to estimate the inflow rate, which is basic and essential input data to design seawater exchange breakwater with a submerged mound by conducting hydraulic model experiments. In the results of multiple regression analysis, log-log equation showed a good agreement rather than linear equation and the estimation of inflow rate was well done with only two parameters except entrance size of the front wall, wave period and the width of water pool. Finally, non-dimensional flow rate equation is derived.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.113-123
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• 1996

Data-dependent (adaptive) choice of asymptotically efficient score functions for rank estimators and M-estimators of regression parameters in a linear regression model with left-truncated and right-censored data are developed herein. The locally adaptive smoothing techniques of Muller and Wang (1990) and Uzunogullari and Wang (1992) provide good estimates of the hazard function h and its derivative h' from left-truncated and right-censored data. However, since we need to estimate h'/h for the asymptotically optimal choice of score functions, the naive estimator, which is just a ratio of estimated h' and h, turns out to have a few drawbacks. An altermative method to overcome these shortcomings and also to speed up the algorithms is developed. In particular, we use a subroutine of the PPR (Projection Pursuit Regression) method coded by Friedman and Stuetzle (1981) to find the nonparametric derivative of log(h) for the problem of estimating h'/h.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.333-351
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• 2022

The skew-t distribution is an attractive family of asymmetrical heavy-tailed densities that includes the normal, skew-normal and Student's-t distributions as special cases. In this work, we propose an EM-type algorithm for computing the maximum likelihood estimates for skew-t linear regression models with censored response. In contrast with previous proposals, this algorithm uses analytical expressions at the E-step, as opposed to Monte Carlo simulations. These expressions rely on formulas for the mean and variance of a truncated skew-t distribution, and can be computed using the R library MomTrunc. The standard errors, the prediction of unobserved values of the response and the log-likelihood function are obtained as a by-product. The proposed methodology is illustrated through the analyses of simulated and a real data application on Letter-Name Fluency test in Peruvian students.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.98-104
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• 2018

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

• Journal of the Korean Chemical Society
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• v.59 no.6
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• pp.483-487
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• 2015

The different calculated quantum chemical descriptors by DFT method were used for prediction of some sulphanilamide Schiff bases inhibitor activity as a binding constant (log K). Multiple linear regression (MLR) and artificial neural network (ANN) were employed for developing the useful quantitative structure activity relationship (QSAR) model. The obtained results presented superiority of ANN model over the MLR one. The offering QSAR model is very easy to computation and Physico-Chemically interpretable. Sensitivity analysis was used to determine the relative importance of each descriptor in ANN model. The order of importance of each descriptor according to this analysis is: molecular volume, molecular weight and dipole moment, respectively. These descriptors appear good information related to different structure of sulphanilamide Schiff bases can participate in their inhibitor activity.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.497-502
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• 2012

This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the $1^{st}$ derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.

• Journal of Adhesion and Interface
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• v.21 no.1
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• pp.6-13
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• 2020

The objective of the current study is to characterize the long-term stability and efficacy of a structural adhesive assembly under static load. An apparatus was designed to be used in the Instron tensile test machine that would allow for real time modeling of the failure characteristics of an assembly utilizing a moisture- cure adhesive which was bonded to concrete. A regression model was developed that followed a linear - natural log function which was used to predict the expected life of the assembly. Evaluations at different curing times confirmed the structure was more robust with longer cure durations prior to loading. Finally, the results show that under the conditions the assembly was tested, there was only a small amount of inelastic creep and the regression models demonstrated the potential for a stable structure lasting several decades.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.10
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• pp.997-1004
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• 2017

The life of elevator wire ropes is one of the most important characteristics of an elevator, which is closely related to the safety of users and its maintenance policy. It is not cost effective to measure the lifetime of elevator wire ropes during their use. In this study, the life estimation of elevator wire ropes (8x19W-IWRC) is considered using accelerated degradation test data. A bending fatigue tester is used to perform the accelerated degradation tests, incorporating the acceleration factor of tensile force. Assuming that the life of wire ropes is log-normally distributed, two life estimation methods are suggested and their results are compared. The first method estimates the life of wire ropes utilizing the accelerated life model with pseudo lives obtained from a linear regression model. The second method estimates the life using a logistic model based on failure probability.

• Agribusiness and Information Management
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• v.8 no.2
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• pp.1-8
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• 2016

The present study has aimed at analyzing the technical and scale efficiencies of credit utilization by the farmer-borrowers in Chittoor district of Andhra Pradesh, India. DEA approach was followed to analyze the credit utilization efficiency and to analyze the factors influencing the credit utilization efficiency, log-linear regression analysis was attempted. DEA analysis revealed that, the number of farmers operating at CRS are more in number in marginal farms (40%) followed by other (35%) and small (17.5%) farms. Regarding the number of farmers operating at VRS, small farmers dominate the scenario with 72.5 per cent followed by other (67.5%) and marginal (42.5%) farmers. With reference to scale efficiency, marginal farmers are in majority (52.5%) followed by other (47.5%) and small (25%) farmers. At the pooled level, 26.7 per cent of the farmers are being operated at CRS, 63 per cent at VRS and 32.5 per cent of the farmers are either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Nearly 58, 15 and 28 percents of the farmers in the marginal farms category were found operating in the region of increasing, decreasing and constant returns respectively. Compared to marginal farmers category, there are less number of farmers operating at CRS both in small farmers category (15%) and other farmers category (22.5%). At the pooled level, only 5 per cent of the farmers are operating at DRS, majority of the farmers (73%) are operating at IRS and only 22 per cent of the farmers are operating at CRS indicating efficient utilization of credit. The log-linear regression model fitted to analyze the major determinants of credit utilization (technical) efficiency of farmer-borrowers revealed that, the three variables viz., cost of cultivation and family expenditure (both negatively influencing at 1% significant level) and family income (positively influencing at 1% significant level) are the major determinants of credit utilization efficiency across all the selected farmers categories and at pooled level. The analysis further indicate that, escalation in the cost of cultivation of crop enterprises in the region, rise in family expenditure and prior indebtedness of the farmers are showing adverse influence on the credit utilization efficiency of the farmer-borrowers.