• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.123-136
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• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.45-60
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• 2014

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.151-157
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• 2014

This study deals with the rear-end collision at roundabouts. The purpose of this study is to develop the accident models of rear-end collision in Korea. In pursuing the above, this study gives particular attention to developing the appropriate models using Poisson, negative binomial model, ZAM, multiple linear and nonlinear regression models, and statistical analysis tools. The main results are as follows. First, the Vuong statistics and overdispersion parameters indicate that ZIP is the most appropriate model among count data models. Second, RMSE, MPB, MAD and correlation coefficient tests show that the multiple nonlinear model is the most suitable to the rear-end collision data. Finally, such the independent variables as traffic volume, ratio of heavy vehicle, number of circulatory roadway lane, number of crosswalk and stop line are adopted in the optimal model.

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.18-33
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• 1988

A new diagnostic statistic for detecting outliers and influential observations in linear models is suggested and studied in this paper. The proposed statistic is a weighted sum of two measures ; one is for detecting outliers and the other is for detecting influential ovservations. The merit of this statistic is that it is possible to distinguish outliers from influential observations. This statistic can be used for not only regression models but also factorial design models. A Monte Carlo simulation study is reported to suggest critical values for detecting outliers and influential observations for simple regression models when the number of observations is 11. 21, 31, 41 or 51.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.523-532
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• 1999

Generalized linear models have various applications for data arising from many kinds of statistical studies. Although the response variable is generally assumed to be generated from a wide class of probability distributions we focus on count data that are most often analyzed using binomial models for proportions or poisson models for rates. The methods and results presented here also apply to many other categorical data models in general due to the relationship between multinomial and poisson sampling. The novelty of the approach suggested here is that all conditional distribution s can be specified directly so that staraightforward Gibbs sampling is possible. The prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage and a vague prior for hyperparameters at the second stage. The methods are demonstrated with an illustrative example using data collected by Rosenkranz and raftery(1994) concerning the number of hospital admissions due to back pain in Washington state.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.125-128
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• 1999

A linear time-invariant model can be described either by a parametric model or by a nonparametric model. Nonparametric models, for which a priori information is not necessary, are basically the response of the dynamic system such as impulse response model and frequency models. Parametric models, such as transfer function models, can be easily described by a small number of parameters. In this paper aiming to take benefit from both types of models, we will use linear-combination of basis fuctions in an impulse response using a few parameters. We will expand and generalize the Kautz functions as basis functions for dynamical system representations and we will consider estimation problem of transfer functions using Kautz function. And so we will present the influences of poles settings of Kautz function on the identification accuracy.

• ETRI Journal
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• v.44 no.4
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.263-277
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• 2024

This paper proposes new parameterizations of the tilted beta binomial distribution, obtained from the combination of the binomial distribution and the tilted beta distribution, where the beta component of the mixture is parameterized as a function of their mean and variance. These new parameterized distributions include as particular cases the beta rectangular binomial and the beta binomial distributions. After that, we propose new linear regression models to deal with overdispersed binomial datasets. These new models are defined from the proposed new parameterization of the tilted beta binomial distribution, and assume regression structures for the mean and variance parameters. These new linear regression models are fitted by applying Bayesian methods and using the OpenBUGS software. The proposed regression models are fitted to a school absenteeism dataset and to the seeds germination rate according to the type seed and root.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1439-1461
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• 2015

Design criteria, modeling rules, and analysis principles of seismic isolation systems have already found place in important building codes and standards such as the Uniform Building Code and ASCE/SEI 7-05. Although real behaviors of isolation systems composed of high damping or lead rubber bearings are nonlinear, equivalent linear models can be obtained using effective stiffness and damping which makes use of linear seismic analysis methods for seismic-isolated buildings possible. However, equivalent linear modeling and analysis may lead to errors in seismic response terms of multi-story buildings and thus need to be assessed comprehensively. This study investigates the accuracy of equivalent linear modeling via numerical experiments conducted on generic five-story three dimensional seismic-isolated buildings. A wide range of nonlinear isolation systems with different characteristics and their equivalent linear counterparts are subjected to historical earthquakes and isolation system displacements, top floor accelerations, story drifts, base shears, and torsional base moments are compared. Relations between the accuracy of the estimates of peak structural responses from equivalent linear models and typical characteristics of nonlinear isolation systems including effective period, rigid-body mode period, effective viscous damping ratio, and post-yield to pre-yield stiffness ratio are established. Influence of biaxial interaction and plan eccentricity are also examined.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.3
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• pp.201-210
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• 2020

2 (Langmuir, Freundlich, Elovich, Temkin, and Dubinin-Radushkevich) and 3 (Sips and Redlich-Peterson)-parameter isotherm models were applied to evaluated for the applicability of adsorption of Cu(II) and/or phosphate isotherm using chitosan bead. Non-linear and linear isotherm adsorption were also compared on each parameter with coefficient of determination (R²). Among 2-parameter isotherms, non-linear Langmuir and Freundlich isotherm showed relatively higher R² and appropriate maximum uptake (q_m) than other isotherm equation although linear Dubinin-Radushkevich obtained highest R². 3-parameter isotherm model demonstrated more reasonable and accuracy results than 2-parmeter isotherm in both non-linear and linear due to the addition of one parameter. The linearization for all of isotherm equation did not increase the applicability of adsorption models when error experiment data was included.