• Animal Bioscience
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• v.34 no.2
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• pp.163-171
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• 2021

Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

• Journal of Forest and Environmental Science
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• v.34 no.2
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• pp.108-118
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• 2018

Biomass determination of species-specific in forest ecosystem by semi-destructive measures requires the development of allometric equations; predict aboveground biomass observable independent variables such as, Diameter at Breast Height, Height, and Volume are crucial role. There has not been equation of this type in mountain Chuqala natural forest. In this study two species namely, Hypericum revolutum Vahl. & Maesa lanceoleta Forssk. with tree diameter classes (15-20, 20.5-25, and 25.5-35 cm), with the purpose of conducting allometric equations were characterized. Each species assumed considered individually. For the linear model fit the two observed variable DBH, H and V were preferred for the prediction of above ground biomass. The best fitted model choose among the two formed model were identified using Akaike Information Criterion (AIC), and $R^2$ and adjacent $R^2$. Based on this the best fit model for Hypericum revolutum Vahl. was AGB=-681.015+4,494.06 (DBH), and for Maesa lanceoleta Forrsk. was. AGB=-936.96+5,268.92 (DBH).

• Korean Journal of Ecology and Environment
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• v.50 no.4
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• pp.374-380
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• 2017

Pine wilt disease (PWD) is one of the hazardous pine tree diseases in whole world. In Korea, PWD has been spreading since it was first observed in Busan in 1988. Dispersion of PWD is mainly mediated by its vectors such as Japanese pine sawyer. In this study, we characterized environmental condition including meteorological factors, geographical factors, and land use factors influencing on the occurrence of PWD. The occurrence data of PWD were collected at 153 sites where were the initial occurrence sites of PWD in local government regions such as city, Gun, or Gu scale. We used Akaike Information Criterion (AIC) to evaluate the relative importance of environmental variables on the discrimination of occurrence or absence of PWD. The results showed that altitude, slope, and distance to road were the most influential factors on the occurrence of PWD, followed by distance to building. Finally, our study presented that human activities highly influenced on the long term dispersal of PWD.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.4
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• pp.317-326
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• 2017

Comparative fishing experiments were conducted in the eastern coastal waters near Uljin, Korea from 2002 to 2004, using the experimental trammel nets to estimate the selectivity for spinyhead sculpin Dasycottus setiger. The inner panels of the nets were made of nylon monofilament with four mesh sizes (82.2, 89.4, 104.8, and 120.2 mm) while its two outer panels were made of twisted nylon multifilament with a mesh size of 510 mm. The SELECT (Share Each Length's Catch Total) procedure with maximum likelihood method was applied to obtain a master selection curve. The different functional models (normal, lognormal, bi-normal, and logistic model) were fitted to the catch data. The lognormal model with the fixed relative fishing intensity was chosen as the best-fitted selection curve through comparison of model deviance and AIC (Akaike's Information Criterion). The optimum relative length (the ratio of fish total length to mesh size) with the maximum relative efficiency was obtained as 2.492.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.143-150
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• 2011

과거의 기업은 단순 생산과 관리만으로 기업을 유지시켜 왔다. 그러나 오늘날 제품 소비자들은 다양하고 복잡한 요구를 하고 있고 기업은 소비자들의 욕구를 충족시키지 못하면 존속할 수가 없다. 따라서 기업은 다양한 소비자의 요구에 대한 사회적, 경제적 환경변화에 대응 할 수 있는 최적의 기본 방침을 수립하고 그것을 실천하는 경영전략 수립이 필요하다. 경영전략 수립은 기업경영성과에 영향을 주는 가장 중요한 요인이라 할 수 있는데, 경영전략 수립과 기업경영성과라는 두 요인 사이에는 또 다른 복잡한 관리체계가 작용한다. 즉 생산시스템과 전사적 품질경영활동, 구성원들에 대한 보상, 그리고 조직구성원간의 관계 등이 영향을 미치는 것이다. 본 연구는 기업의 경영전략과 관리체계인 생산시스템, TQM 활동과 종업원에 대한 보상이 경영성과에 어떠한 영향이 있는지를 파악하고 이것을 기반으로 실증적 자료를 수집하여 분석 검증함으로써 기업발전의 토대로 삼고자 한다. 자료 분석을 위하여 SPSS 통계 패키지를 이용하였고, 각 연구 가설과 연구 모형은 구조방정식을 이용하여 검증하였다.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.335-342
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• 2015

Joint hierarchical generalized linear models proposed by Molas et al. (2013) extend the simple longitudinal model into multiple models fitted jointly. It can easily handle the correlation of multivariate longitudinal data. In this paper, we apply this method to analyze KoGES cohort dataset. Fixed unknown parameters, random effects and variance components are estimated based on a standard framework of h-likelihood theory. Furthermore, based on the conditional Akaike information criterion the correlated covariance structure of random-effect model is selected rather than an independent structure.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1103-1110
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• 2020

In this study, a segmented model with Upside-Down bathtub shaped failure intensity for a repairable system are proposed under the assumption that the occurrences of the failures of a repairable system follow the Non-Homogeneous Poisson Process. The proposed segmented model is the compound model of S-PLP and LIP (Segmented Power Law Process and Logistic Intensity Process), that fits the separate failure intensity functions on each segment of time interval. The maximum likelihood estimation is used for estimating the parameters of the S-PLP and LIP model. The case study of system A shows that the S-PLP and LIP model fits better than the other models when compared by AICc (Akaike Information Criterion corrected) and MSE (Mean Squared Error). And it also implies that the S-PLP and LIP model can be useful for explaining the failure intensities of similar systems.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.