• Journal of the Korean Ceramic Society
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• v.36 no.1
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• pp.50-54
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• 1999

We have measured the x-ray photoemission spectroscopy of cation deficient La0.970Mn0.970O3 as a function of temperature. Detailed results on the chemical shifts and changes in Mn 2p and Lp 3d core levels due to variation of temperature have been obtained. The Mn 2p 3/2 and 1/2 main peaks and La 3d core spectrum shift to lower binding energy levels with increasing temperature. This XPS behavior is correlated with the strength of localization of Mn3+. The Jahn-Teller effect due to Mn3+ besides the conventional random potential effects is likely to localize charge carriers in La-.970Mn0.970O3.

• 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.31 no.2
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• pp.287-301
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• 2018

It is common to encounter count data with excess zeros in various research fields such as the social sciences, natural sciences, medical science or engineering. Such count data have been explained mainly by zero-inflated Poisson model and extended models. Zero-inflated count data are also often correlated or clustered, in which random effects should be taken into account in the model. Frequentist approaches have been commonly used to fit such data. However, a Bayesian approach has advantages of prior information, avoidance of asymptotic approximations and practical estimation of the functions of parameters. We consider a Bayesian zero-inflated Poisson regression model with random effects for correlated zero-inflated count data. We conducted simulation studies to check the performance of the proposed model. We also applied the proposed model to smoking behavior data from the Regional Health Survey (2015) of the Korea Centers for disease control and prevention.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1327-1334
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• 2008

Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• Nutritional Sciences
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• v.9 no.4
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• pp.280-287
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• 2006

The aims of this study were to describe the effects of glucose-sweetened drinks on blood glucose, energy, and water intake at a meal 3 hours later. The effect of blood glucose on prandial energy intake and the relationship between water and energy intake during a meal were also determined. Twenty healthy normal-weight men were fed pizza test meals 3h after consuming four drinks of 0, 50g, 65g, and 75g glucose in random order, within-subjects design. Blood samples were measured at baseline and every 30 min after ingestion of drinks and 30min after the end of the test meal and the appetite was also assessed by visual analog test at the same interval. The results of this study showed that various glucose drinks altered blood glucose responses compared with that of water control(p<0.0001). Blood glucose areas under the curve(AUC) for glucose-sweetened drinks were significantly(p<0.05) higher than that for the control over 3 hours after a drink and 30 min after the test meal. Consumption of the glucose-sweetened drinks significantly increased(p<0.05) energy and water intake at a test meal compared with the water control, except the drink containing 75g glucose. For all drinks combined, the energy intake was negatively correlated with the blood glucose and positively correlated with the volume of water consumed at a test meal at 3 hours later.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Structural Engineering and Mechanics
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• v.15 no.4
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• pp.381-394
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• 2003

While constructing multistorey buildings with reinforced concrete framed structures it is a common practice to provide parking space for vehicles at the ground floor level. This floor will generally consist of open frames without any infilled walls and is called an open-storey. From a post disaster damage survey carried out, it was noticed that during the January 26, 2001 Bhuj (Gujarat, India) earthquake, a large number of reinforced concrete framed buildings with open-storey at ground floor level, suffered extensive damage and in some cases catastrophic collapse. This has brought into sharp focus the need to carry out systematic studies on the seismic vulnerability of such buildings. Determination of vulnerability requires realistic structural response estimations taking into account the stochasticity in the loading and the system parameters. The stochastic finite element method can be effectively used to model the random fields while carrying out such studies. This paper presents the details of stochastic finite element analysis of a five-storey three-bay reinforced concrete framed structure with open-storey subjected to standard seismic excitation. In the present study, only the stochasticity in the system parameters is considered. The stochastic finite element method used for carrying out the analysis is based on perturbation technique. Each random field representing the stochastic geometry/material property is discretised into correlated random variables using spatial averaging technique. The uncertainties in geometry and material properties are modelled using the first two moments of the corresponding parameters. In evaluating the stochastic response, the cross-sectional area and Young' modulus are considered as independent random fields. To study the influence of correlation length of random fields, different correlation lengths are considered for random field discretisation. The spatial expectations and covariances for displacement response at any time instant are obtained as the output. The effect of open-storey is modelled by suitably considering the stiffness of infilled walls in the upper storey using cross bracing. In order to account for changes in soil conditions during strong motion earthquakes, both fixed and hinged supports are considered. The results of the stochastic finite element based seismic analysis of reinforced concrete framed structures reported in this paper demonstrate the importance of considering the effect of open-storey with appropriate support conditions to estimate the realistic response of buildings subjected to earthquakes.

• Journal of Nutrition and Health
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• v.36 no.8
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• pp.870-881
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• 2003

We examined the effects of 5 g red pepper powder in soup preload given at breakfast on food intake, blood pressure, body core temperature, hunger, fullness and thirst scores in 29 female collage students. All subjects received two kind of soup preloads in random order. After ingesting a soup, subjects ate other food items as a breakfast ad libitum. Two soups were of the same composition and volume but differed only in 5 g red pepper. So one soup designated as "beef-vegetable" and the other soup designated as "red pepper". Red pepper soup consumption significantly enhanced energy and macronutrient intake by 17%. The hunger scores after test meals were inversely correlated with energy and nutrient intake in beef-vegetable meal. However, the postprandial hunger scores were not correlated with energy and nutrient intakes in red pepper meal. The fullness scores at 90 min after the red pepper meal were inversely correlated with energy and nutrient intake whereas the fullness scores after beef-vegetable meal were not correlated with energy and nutrient intake. These results suggest that hot red pepper ingestion may desensitize some gastrointestinal vagal afferents and disturb feeling of hunger and fullness. The postprandial changes of body temperatures in red pepper meal were higher for a longer time in comparison with those in beef-vegetable meal. For the red pepper meal there frequently were higher correlations between blood pressures and anthropometric measurements, compared to those in beef-vegetable meal. These results might be explained partly by the enhancing effects of capsaicin on thermogenesis and sympathetic nervous system activity. It is concluded that the ingestion of spicy soup with red pepper can increase appetite, energy and nutrient intakes in Korean females, and this effect might be related to disturbed feeling of hunger and fullness.hunger and fullness.