• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.457-469
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• 1996

In this paper, we propose a method for an exact test of H : $p_i$ = $r_i$ for all i against K : $p_i$ $\neq$ $r_i$ for some i in an unbalanced random effect linear model, where $p_i$ denotes the ratio of the i-th variance component to the error variance. Then we present a method to test H : $p_i$ $\leq$ r against K : $p_i$> r for some specific i by applying orthogonal projection on the model. We also show that any test statistic that follows an F-distribution on the boundary of the hypotheses is equal to the one given here.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.4
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• pp.457-462
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• 2011

The purpose of this study was to detect QTL for carcass quality on bovine chromosome (BTA) 6 using a high density SNP map in a Hanwoo population. The data set comprised 45 sires and their 427 Hanwoo steers that were born between spring of 2005 and fall of 2007. The steers that were used for progeny testing in the Hanwoo Improvement Center in Seosan, Korea, were genotyped with the 2,535SNPs on BTA6 that were embedded in the Illumina bovine SNP 50K chip. Four different linkage disequilibrium (LD) mapping models were applied to detect significant SNPs for carcass quality traits; the fixed model with a single marker, the random model with a single marker, the random model with haplotype effects using two adjacent markers, and the random model at hidden state. A total of twelve QTL were detected, for which four, one, three and four SNPs were detected on BTA6 under the respective models (p<0.001). Among the detected QTL, four, two, five and one QTL were associated with carcass weight, backfat thickness, longissimus dorsi muscle area, and marbling score, respectively (p<0.001). Our results suggest that the use of multiple LD mapping approaches may be beneficial in increasing power to detect QTL given a limited sample size and magnitude of QTL effect.

• Coupled systems mechanics
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• v.12 no.1
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• pp.1-20
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• 2023

On the basis of the explicit time-domain method, an investigation is performed on the influence of the rotational stiffness and rotational damping of the vehicle body and front-rear bogies on the dynamic responses of the vehicle-bridge coupled systems. The equation of motion for the vehicle subsystem is derived employing rigid dynamical theories without considering the rotational stiffness and rotational damping of the vehicle body, as well as the front-rear bogies. The explicit expressions for the dynamic responses of the vehicle and bridge subsystems to contact forces are generated utilizing the explicit time-domain method. Due to the compact wheel-rail model, which reflects the compatibility requirement of the two subsystems, the explicit expression of the evolutionary statistical moment for the contact forces may be performed with relative ease. Then, the evolutionary statistical moments for the respective responses of the two subsystems can be determined. The numerical results indicate that the simplification of vehicle model has little effect on the responses of the bridge subsystem and the vehicle body, except for the responses of the rotational degrees of freedom for the vehicle subsystem, regardless of whether deterministic or random analyses are performed.

• 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.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.545-558
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• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.901-905
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• 2012

In this study, we investigated the pure geometrical effect of porous materials in gas adsorption using the grand canonical Monte Carlo simulations of primitive gas-pore models with various pore geometries such as planar, cylindrical, and random pore geometries. Although the model does not possess atomistic level details of porous materials, our simulation results provided many insightful information in the effect of pore geometry on the adsorption behavior of gas molecules. First, the surface curvature of porous materials plays a significant role in the amount of adsorbed gas molecules: the concave surface such as in cylindrical pores induces more attraction between gas molecules and pore, which results in the enhanced gas adsorption. On the contrary, the convex surface of random pores gives the opposite effect. Second, this geometrical effect shows a nonmonotonic dependence on the gas-pore interaction strength and length. Third, as the external gas pressure is increased, the change in the gas adsorption due to pore geometry is reduced. Finally, the pore geometry also affects the collision dynamics of gas molecules. Since our model is based on primitive description of fluid molecules, our conclusion can be applied to any fluidic systems including reactant-electrode systems.

• Physical Therapy Rehabilitation Science
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• v.12 no.1
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• pp.12-18
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• 2023

Objective: Chemotherapy is usually given to inhibit cancer progression. It is the most common side effect of chemotherapyinduced peripheral neuropathy (CIPN) after chemotherapy, and its symptoms include pain such as paresthesia, dysesthesia, allodynia, hyperalgesia, and electrical stimulation. Therefore, in this review, randomized controlled trials (RCTs) were combined to analyze the effect qualitatively and quantitatively in order to find out the effect of manual therapy on patients with CIPN through a meta-analysis. Design: A systematic review and meta-analysis Methods: This review conducted a literature search through international databases (CINAHL, Embase, MEDLINE, Web of Science) in December 2022 to synthesize the effect of manual therapy on the symptomatic improvement of CIPN. Qualitative evaluation (risk of bias) and quantitative evaluation using ReVMan provided by the Cochrane Group were expressed as a random effect model and standardized mean difference (SMD). Results: In four RCTs 165 patients with CIPN were evaluated for symptoms of neuropathy. The experimental group consisting of manual therapy and its subcategories showed significant improvement compared to the control group. The results analyzed through the random effects model were SMD=-1.11; 95% confidence interval, -1.97 to -0.24. Conclusions: We came to the conclusion that manual therapy could significantly contribute to improving the symptoms of CIPN, and since it may vary depending on the technique of manual therapy, further studies on manual therapy suitable for neuropathy are needed.

• 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.

• 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.

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.207-218
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• 2010

The purpose of the study is to predict the shipping business during the period of 2011 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2010. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors of all the ARIMA-type models are somewhat higher than normally expected. Furthermore, the random walk model outperforms all the ARIMA-type models. This reveals that the BDI is just a random walk phenomenon and it's meaningless to predict the BDI using various econometric techniques. The ARIMA-type models show that the shipping market is expected to be bearish in 2011. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.