• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.787-796
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• 2009

A random coefficient model is applied when times of the repeated measurements are not fixed in experiments with respect to the subjects. The procedures of the inference of a random coefficient model are same as those of a mixed model. Diet experimental data was used for applying the random coefficient model. Various random coefficient models are investigated for the experimental data, and are compared each other. Finally, optimal random coefficient model would be selected. It resulted from the analysis that for the fixed effect factor, the baseline, treatment, height, and time effect were very significant. The treatment effect of the diet foods and exercises were more effective in losing weight than the effect of the diet foods only. The fixed cubic time effect was very significant. The variance components corresponding to the subject effect, linear time effect, quadratic time effect, and cubic time effect of the random coefficients are all positive. When quartic time effect was added as random coefficients the model did not converge. Thus random coefficients up to the cubic terms was considered as the optimal model.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.213-218
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• 2017

The purpose of this study was to develop age-biomass model in Chungnam region containing random effect. To develop the biomass model by species and tree component, data for Pinus densiflora in central region is collected to 30 plots (150 trees). The mixed model were used to fixed effect in the age-biomass relation for Pinus densiflora, with random effect representing correlation of survey area were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -1.0022, 0.6240, respectively. The model with random effect (AIC=377.2) has low AIC value, comparison with other study relating to random effects. It is for this reason that random effect associated with categorical data were used in the data fitting process, the model can be calibrated to fit the Chungnam region by obtaining measurements. Therefore, the results of this study could be useful method for developing biomass model using random effects by region.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.302-308
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• 2014

In this paper, a detailed investigation of the random vibration test is presented for strapdown inertial navigation systems (INS). The effect of the random vibration test has been studied from the point of view of navigation performance. The role of distribution functions and RMS value is represented to determine a feasible method to reject or reduce vibration related error in position and velocity estimation in inertial navigation. According to a survey conducted by the authors, this is the first time that the effect of the distribution function in vibration related error has been investigated in random vibration testing of INS. Recorded data of navigation grade INS is used in offline static navigation to examine the effect of different characteristics of random vibration tests on navigation error.

• Korea Trade Review
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• v.41 no.3
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• pp.177-198
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• 2016

China has retained economic growth rate of average 9% for more than ten years recently after China introduced capitalistic market economy system in 1979 by Deng Xiaoping. China has attracted foreign direct investment for a long time because it has retained very high economic growth rate, low labor cost, and various policies for foreign investors. This paper tries to analyse the determinants of foreign direct investment inflow after reform-opening of China with empirical analysis methods utilizing each province·city's specific characteristics by using the panel data from 1985 to 2013. For the empirical analysis we use random effect model, fixed effect model, pooled OLS, and random coefficient model. The results by pooled OLS and random coefficient model are presented for the comparison with the main results in the process of research. The research shows the results by fixed effect model are better than those by random effect model after doing Hausman's test. The results shows that GRDP, capital stock, and telecommunication exert a positive relationship with foreign direct investment, while express way variable exerts a negative one. China's education level surprisingly does not attract foreign direct investment even though it is not at a critical level. Therefore, the Chinese government should try to increase national income level as it symbolizes market size; encourage domestic investment; and construct high quality telecommunication infrastructure.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.741-757
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• 2000

In may experimental situations, whenever a block design is used, the block effect is usually considered to be fixed. There are, however, experimental situations in which it should be treated as random. The choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of he prediction variance even if the experimental runs re the same. Therefore, care should be exercised in the selection of blocks. In this paper, in the presence of a random block effect, we propose a graphical method or evaluating the effect of blocking in response surface designs using cuboidal regions. This graphical method can be used to investigate how the blocking has influence on the prediction variance throughout all experimental regions of interest when this region is cuboidal, and compare the block effects in the cases of the orthogonal and non-orthogonal block designs, respectively.

• Bulletin of the Korean Chemical Society
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• v.35 no.4
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• pp.1057-1060
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• 2014

In our previous articles, an approach has been proposed for the evaluation of the uncertainty of overall result from multiple measurements. In the approach, uncertainty sources were classified into two groups: the first including those giving same 'systematic' effect on each individual measurement and the second including the others giving 'random' effect on each individual measurement and causing a variation among individual measurement results. The arithmetic mean of the replicated measurements is usually assigned as the value for the overall result. Uncertainty of the overall result is determined by separately evaluating and combining an overall uncertainty from sources of the 'systematic' effect and another overall uncertainty from sources of the 'random' effect. This conceptual approach has been widely adopted in chemical metrology society. In this study, further logical proof with more detailed mathematical expressions is provided on the approach.

• 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.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.523-542
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• 2007

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.481-482
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• 2022

In this paper, effect of random dopant fluctuation (RDF) of the top-transistor in a monolithic 3D inverter composed of MOSFET transistors is investigated with 3D TCAD simulation when the gate voltage of the bottom-transistor is changed. The sampling for investigating RDF effect was conducted through the kinetic monte carlo method, and the RDF effect on the threshold voltage variation in the top-transistor was investigated, and the electrical coupling between top-transistors and bottom-transistors was investigated.