• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.523-533
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• 2020

This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.37-37
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• 2006

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.971-978
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• 2009

Poisson generalized linear mixed models(GLMMs) have been widely used for the analysis of clustered or correlated count data. For the inference marginal likelihood, which is obtained by integrating out random effects is often used. It gives maximum likelihood(ML) estimator, but the integration is usually intractable. In this paper, we propose how to obtain the ML estimator via Laplace approximation based on hierarchical-likelihood (h-likelihood) approach under the Poisson GLMMs. In particular, the h-likelihood avoids the integration itself and gives a statistically efficient procedure for various random-effect models including GLMMs. The proposed method is illustrated using two practical examples and simulation studies.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Computers and Concrete
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• v.12 no.5
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• pp.585-612
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• 2013

The paper presents quasi-static numerical simulations of the behaviour of short reinforced concrete beams without shear reinforcement under mixed shear-tension failure using the FEM and four various constitutive continuum models for concrete. First, an isotropic elasto-plastic model with a Drucker-Prager criterion defined in compression and with a Rankine criterion defined in tension was used. Next, an anisotropic smeared crack and isotropic damage model were applied. Finally, an elasto-plastic-damage model was used. To ensure mesh-independent FE results, to describe strain localization in concrete and to capture a deterministic size effect, all models were enhanced in a softening regime by a characteristic length of micro-structure by means of a non-local theory. Bond-slip between concrete and reinforcement was considered. The numerical results were directly compared with the corresponding laboratory tests performed by Walraven and Lehwalter (1994). The advantages and disadvantages of enhanced models to model the reinforced concrete behaviour were outlined.

• Structural Engineering and Mechanics
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• v.34 no.5
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• pp.563-580
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• 2010

Composite beams using bolts to attach steel plates to the side faces of existing reinforced concrete (RC) coupling beams can enhance both their strength and deformability. The behavior of those composite beams differs substantially from the behavior of typical composite beams made up of steel beams and concrete slabs. The former are subjected to longitudinal, vertical and rotational slips, while the latter only involve longitudinal slip. In this study, a mixed analysis method was adopted to develop the fundamental equations for accurate prediction of the load-carrying capacity of steel plate strengthened RC coupling beams. Then, a rigid plastic analysis technique was used to cope with the full composite effect of the bolt group connections. Two theoretical models for the determination of the strength of medium-length plate strengthened coupling beams based on mixed analysis and rigid plastic methods are presented. The strength of the strengthened coupling beams is derived. The vertical and longitudinal slips of the steel plates and the shear strength of the anchor-bolt connection group is considered. The theoretical models are validated by the available experimental results presented in a companion paper. The strength of the specimens predicted from the mixed analysis model is found to be in good agreement with that from the experimental results.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.701-714
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• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• Geomechanics and Engineering
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• v.33 no.5
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• pp.439-451
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• 2023

The construction of shield tunnelling in urban sites is facing serious risks from complex and changeable underground conditions. Construction problems in the sand-clay mixed ground have been more reported in recent decades for its poor control of soil loss in tunnel face, ground settlement and supporting pressure. Since the limitations of observation methods, the conventional physical modelling experiments normally simplify the tunnelling to a plane strain situation whose results are not reliable in mixed ground cases which exhibit more complicated responses. We propose a new method for the study of the mixed ground tunnel through which mixed lays are simulated with transparent soil surrogates exhibiting different mechanical properties. An experimental framework for the transparent soil modelling of the mixed ground tunnel was established incorporated with the self-developed digital image correlation system (PhotoInfor). To understand better the response of face stability, ground deformation, settlement and supporting phenomenon to tunnelling excavation in the sand-clay mixed ground, a series of case studies were carried out comparing the results from cases subjected to different buried depths and mixed phenomenon. The results indicate that the deformation mode, settlement and supporting phenomenon vary with the mixed phenomenon and buried depth. Moreover, a stratigraphic effect exists that the ground movement around mixed face reveals a notable difference.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.71-79
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• 2013

Semiparametric and nonparametric small area estimations have been studied to overcome a large variance due to a small sample size allocated in a small area. In this study, we investigate semiparametric and nonparametric mixed effect small area estimators using penalized spline and kernel smoothing methods respectively and compare their performances using labor statistics.

• Journal of Radiation Protection and Research
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• v.46 no.3
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• pp.83-97
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• 2021

Background: The lifetime risk of lung cancer incidence due to radiation for nonsmokers is overestimated because of the use of the average cancer baseline risk among a mixed population, including smokers. In recent years, the generalized multiplicative (GM)-excess relative risk (ERR) model has been developed in the life span study of atomic bomb survivors to consider the joint effect of radiation and smoking. Based on this background, this paper discusses the issues of radiation risk assessment considering smoking in two parts. Materials and Methods: In Part 1, we proposed a simple method of estimating the baseline risk for nonsmokers using current smoking data. We performed sensitivity analysis on baseline risk estimation to discuss the birth cohort effects. In Part 2, we applied the GM-ERR model for Japanese smokers to calculate lifetime attributable risk (LAR). We also performed a sensitivity analysis using other ERR models (e.g., simple additive (SA)-ERR model). Results and Discussion: In Part 1, the lifetime baseline risk from mixed population including smokers to nonsmokers decreased by 54% (44%-60%) for males and 24% (18%-29%) for females. In Part 2, comparison of LAR between SA- and GM-ERR models showed that if the radiation dose was ≤200 mGy or less, the difference between these ERR models was within the standard deviation of LAR due to the uncertainty of smoking information. Conclusion: The use of mixed population for baseline risk assessment overestimates the risk for lung cancer due to low-dose radiation exposure in Japanese males.