• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2017.05a
• /
• pp.154-156
• /
• 2017

Constant variance of degradation data over time has been generally assumed to estimate storage lifetime using destructive, accelerated degradation data over time. However, performance data of ammunitions deteriorate over time, and the standard deviation would tend to increase over time. This paper shows storage lifetime comparison results for constant variance and time-variant variance assumptions of degradation data over time, and proposes that time-variant variance assumption should be considered to increase accuracy in lifetime estimation.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.6
• /
• pp.1041-1049
• /
• 2010

In Korean professional baseball, attendance is the largest source of revenue for development of professional baseball and the highest concern of professional baseball teams. So, if there is demand forecasting model, it will be helpful for pennant chasers to work out the strategies for drawing attendance. For this reason, this research intends to suggest the model which estimates Korean professional baseball's attendance and uses all usable variables which have an effect on attendance in limited circumstances. We supposed that dependent variable is attendance as well as several independent variables and error term are homoscedastic variance. And then, we compared the models which assume conditional heteroscedastic variance like GARCH and EGARCH with GARCH-t models which use the assumption that error term's distribution follows student-t distribution. In result of that, we could confirm that the models which were made by using GARCH(1,1)-t made estimates the most accurately among the several models considered.

• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1213-1224
• /
• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.3
• /
• pp.547-557
• /
• 2017

The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.

• The Korean Journal of Applied Statistics
• /
• v.37 no.3
• /
• pp.283-295
• /
• 2024

Many studies have been conducted to properly handle nonresponse. Recently, many nonresponse imputation methods have been developed and practically used. Most imputation methods assume MCAR (missing completely at random) or MAR (missing at random). On the contrary, there are relatively few studies on imputation under the assumption of MNAR (missing not at random) or NN (nonignorable nonresponse) that are affected by the study variable. The MNAR causes Bias and reduces the accuracy of imputation whenever response probability is not properly estimated. Lee and Shin (2022) proposed a nonresponse imputation method that can be applied to nonignorable nonresponse assuming homoscedasticity in super-population model. In this paper we propose an generalized version of the imputation method proposed by Lee and Shin (2022) to improve the accuracy of estimation by removing the Bias caused by MNAR under heteroscedasticity. In addition, the superiority of the proposed method is confirmed through simulation studies.

• Korean Journal of Ichthyology
• /
• v.13 no.2
• /
• pp.93-99
• /
• 2001

Skin mucous cells in the three regions of the body in Pseudobagrus fulvidaraco and Leiocassius nitidus were investigated using three histochemical methods (PAS, AB-PAS and HID). In the two species, components of mucous cell were not distinguished in each region, but presented a differences in position and type, size, and count in all regions. Mucous cells of P. fulvidraco were located in two layers, a superficial epithelial cell and the space within alarm substance cells (ASCs). Mucous cells of L. nitidus were situated only in the superficial epithelial cell layer. The size of mucous cells in the ASC layer were larger than those of the superficial epithelial cells for all measured values: diameter, length, width, and area. In the superficial epithelial cell layer, the average number of mucous cells within the three regions did not differ significantly between species by unpaired T-test, but the average number of those within two cell layers of P. fulvidraco had a similarity at each region by paired T-test. The dismilarity in dorsal and ventral regions in average number of ASCs between P. fulvidraco and L. niditus was confirmed by unpaired T-test.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.2
• /
• pp.173-179
• /
• 2018

Estimating reliability of a non-repairable system using the degradation data, variance assumption such as homogeneity (constant) or heteroscedasticity (time-variant) could affect accuracy of reliability estimation. This paper showed reliability estimation and comparison results under normal conditions using accelerated degradation data obtained from destructive measurements, according to variance assumption of the data at each measurement time. Degradation data from three accelerated conditions with stress factors of temperature and humidity were used to estimate reliability. The $B_{10}$ lifetime was estimated as 1243.8 years by constant variance assumption, and 18.9 years by time-variant variance. And variance assumption provided different analysis results of important stresses to reliability. Thus, accurate assumption of variance at each measurement time is required when estimating reliability using degradation data of a non-repairable system.

• The Korean Journal of Applied Statistics
• /
• v.29 no.4
• /
• pp.595-611
• /
• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• The Korean Journal of Applied Statistics
• /
• v.36 no.4
• /
• pp.295-307
• /
• 2023

The heterogeneous autoregressive (HAR) model is a simple linear model that is commonly used to explain long memory in the realized volatility. However, as realized volatility has more complicated features such as conditional heteroscedasticity, leverage effect, and volatility clustering, it is necessary to extend the simple HAR model. Therefore, to better incorporate the stylized facts, we propose a threshold HAR model with GARCH errors, namely the THAR-GARCH model. That is, the THAR-GARCH model is a nonlinear model whose coefficients vary according to a threshold value, and the conditional heteroscedasticity is explained through the GARCH errors. Model parameters are estimated using an iterative weighted least squares estimation method. Our simulation study supports the consistency of the iterative estimation method. In addition, we show that the proposed THAR-GARCH model has better forecasting power by applying to the realized volatility of major 21 stock indices around the world.

• Journal of Korean Society of Environmental Engineers
• /
• v.28 no.11
• /
• pp.1198-1206
• /
• 2006

We have carried out highway traffic noise prediction and measurement for 10 sites with representative road shapes and structures. A road traffic noise prediction model(NIER('99)) has been developed for environmental impact assessment in Korea. With the fitted regression analysis, the distribution ratio($R^2$) and Pearson correction coefficient(r) was 92.4% and 0.96 in $1^{st}$ floor, 38.7% and 0.66 in $3^{rd}$ floor, 42% and 0.65 in $5^{th}$ floor, 7.5% and 0.27 in $7^{th}$ floor, 28.4% and 0.53 in 10th floor, 35.6% and 0.60 in $13^{th}$ floor, 52.7% and 0.73 in $15^{th}$ floor, respectively. The measured values of the noise level except the 1st floor did not show a good agreement with the predicted noise level in the NIER('99) formula. Also, the NIER('99) formula demonstrated that the measured values weren't reasonably close to the predicted values, indicating the validity and adequacy of the predicted models with the fitted vs residual analysis in the 95% of confidence interval and 95% of predict interval. Using the equal variation on the basis of the residual vs fitted value, there was the significant difference for variation between $3^{rd}$ floor and $15^{th}$ floor except $1^{st}$ floor. The results suggested that the NIER('99) model obtained by the results according to the apartment floor must be improved and developed on the road traffic noise.