• Journal of Distribution Science
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• v.17 no.3
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.65-73
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• 2023

Contemporary biomedical data often involve an ill-posed problem owing to small sample size and large number of multi-collinear variables. Partial least squares (PLS) method could be a plausible alternative to an ill-conditioned ordinary least squares. However, in the case of a PLS model that includes a random-effect, how to deal with a random-effect or mixed effects remains a widely open question worth further investigation. In the present study, we propose a modified multivariate PLS method implementing mixed-effect model (PLSM). The advantage of PLSM is its versatility in handling serial longitudinal data or its ability for taking a randomeffect into account. We conduct simulations to investigate statistical properties of PLSM, and showcase its real clinical application to predict treatment outcome of esthetic surgical procedures of human faces. The proposed PLSM seemed to be particularly beneficial 1) when random-effect is conspicuous; 2) the number of predictors is relatively large compared to the sample size; 3) the multicollinearity is weak or moderate; and/or 4) the random error is considerable.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.2
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• pp.160-165
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• 2007

Dispersion parameters for the number of piglets born alive were estimated using a repeatability and random regression model. Six sow breeds/lines were included in the analysis: Swedish Landrace, Large White and both crossbred lines between them, German Landrace and their cross with Large White. Fixed part of the model included sow genotype, mating season as month-year interaction, parity and weaning to conception interval as class effects. The age at farrowing was modelled as a quadratic regression nested within parity. The previous lactation length was fitted as a linear regression. Random regressions for parity on Legendre polynomials were included for direct additive genetic, permanent environmental, and common litter environmental effects. Orthogonal Legendre polynomials from the linear to the cubic power were fitted. In the repeatability model estimate of heritability was 0.07, permanent environmental effect as ratio was 0.04, and common litter environmental effect as ratio was 0.01. Estimates of genetic parameters with the random regression model were generally higher than in the repeatability model, except for the common litter environmental effect. Estimates of heritability ranged from 0.06 to 0.10. Permanent environmental effect as a ratio increased along a trajectory from 0.03 to 0.11. Magnitudes of common litter effect were small (around 0.01). The eigenvalues of covariance functions showed that between 7 and 8 % of genetic variability was explained by individual genetic curves of sows. This proportion was mainly covered by linear and quadratic coefficients. Results suggest that the random regression model could be used for genetic analysis of litter size.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.269-279
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• 2015

This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.19-30
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• 2006

For highly reliable products, it is difficult to assess the lifetime of the products with traditional life tests. Accordingly, a recent approach is to observe the performance degradation of product during the test rather than regular failure time. This study compares performances of three methods(i.e. the approximation, analytical and numerical methods) to estimate the parameters and quantiles of the lifetime when the time-to-failure distribution follows Weibull and lognormal distributions under a random coefficient degradation rate model. Numerical experiments are also conducted to investigate the effects of model error such as measurements in a random coefficient model.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.119-126
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• 2018

PURPOSES : The purpose of this study is to develop a crash prediction model at signalized intersections, which can capture the randomness and uncertainty of traffic accident forecasting in order to provide more precise results. METHODS : The authors propose a random parameter (RP) approach to overcome the limitation of the Count model that cannot consider the heterogeneity of the assigned locations or road sections. For the model's development, 55 intersections located in the Daejeon metropolitan area were selected as the scope of the study, and panel data such as the number of crashes, traffic volume, and intersection geometry at each intersection were collected for the analysis. RESULTS : Based on the results of the RP negative binomial crash prediction model developed in this study, it was found that the independent variables such as the log form of average annual traffic volume, presence or absence of left-turn lanes on major roads, presence or absence of right-turn lanes on minor roads, and the number of crosswalks were statistically significant random parameters, and this showed that the variables have a heterogeneous influence on individual intersections. CONCLUSIONS : It was found that the RP model had a better fit to the data than the fixed parameters (FP) model since the RP model reflects the heterogeneity of the individual observations and captures the inconsistent and biased effects.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.211-219
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• 2015

Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

• 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.