• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.295-302
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• 2000

We study the efficient method to estimate the parameters for the Weibull model with covariates which occupies an important position in survival analysis. A treatment period may be divided by the stages of treatments under the different treatment arams. The piecewise method is considered to obtain the estimators of the parameters by maximum likelihood method. We explore the real data to show that the piecewise is more efficient than the nonpiecewise to estimate the parameters.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.431-440
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• 2013

We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.

• 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.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.275-292
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• 2010

Receiver operating characteristic(ROC) curves can be used to assess the accuracy of tests measured on ordinal or continuous scales. The most commonly used measure for the overall diagnostic accuracy of diagnostic tests is the area under the ROC curve(AUC). When two ROC curves are constructed based on two tests performed on the same individuals, statistical analysis on differences between AUCs must take into account the correlated nature of the data. This article focuses on confidence interval estimation of the difference between paired AUCs. We compare nonparametric, maximum likelihood, bootstrap and generalized pivotal quantity methods, and conduct a monte carlo simulation to investigate the probability coverage and expected length of the four methods.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1145-1152
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• 2011

This study was conducted to estimate genetic effects on economically important traits for genetic improvement in Hanwoo by using the real-time ultrasound measurements for longissimus dorsi muscle area (LMA), backfat thickness (BFT), and marbling score (Marb). The phenotypic data were obtained from 1,648 pedigreed cows, and general linear models were applied to test the effects of age, region, and body condition socre. The cows between 50 and 60 months of age had the greatest scores for LMA and BFT, and Marb (P<0.05). The cows in region C had the greatest scores for body condition socre, LMA and BFT, while in region J Marb was the lowest (P<0.05). There was positive relation with LMA, BFT, and Marb according to increase body condition socre. Heritabilities for LMA, BFT, and Marb were estimated as 0.136, 0.351, and 0.236, respectively. These results would provide primary information for the efficient implementation of genetic improvement schemes in Hanwoo.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1001-1012
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• 2016

It is well known that air pollutants exert a bad influence on human health. According to the United Nations Environment Program, 4.3 million people die from carbon monoxide and particulate matter annually from all over the world. Carbon monoxide is a toxic gas that is the most dangerous of the gas consisting of carbon and oxygen. In this paper, we used 1 hour, 6 hours, 12 hours, and 24 hours average carbon monoxide concentration data collected between 2004 and 2013 in Daegu Gyeongbuk area. Parameters of the generalized extreme value distribution were estimated by maximum likelihood estimation and L-moments estimation. An evalution of goodness of fitness also was performed. Since the number of samples were small, L-moment estimation turned out to be suitable for parameter estimation. We also calculated 5 year, 10 year, 20 year, and 40 year return level.

• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.325-332
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• 2011

This research was conducted to analyze the effects of raising farm on the heritability and breeding values of Hanwoo cows for their carcass traits, including cold carcass weight (CWT), back-fat thickness (BFT), eye-muscle area (EMA) and marbling score (MAR). The carcass data and pedigree data were collected from steers raised on Hanwoo farms in Pyeongchang-gun, Gangwon-do, South Korea. Three analytical models were applied for the estimation of heritabilities and breeding values. The first model (model 1) included slaughter house-year-month combination as fixed effects and age at slaughter was fitted as linear and quadratic covariates. The second model (model 2) was similar to model 1, but raising farm was additionally included as random effect. The third model (model 3) was similar to model 1 but farm effects were additionally included as fixed effect. The comparisons between the model 1 and the models including farm effect (model 2 and model 3) revealed that heritability estimates from model 2 or model 3 were smaller to those from model 1 for all carcass traits. Especially, obvious decrease of heritability was observed in CWT where heritability was 0.23 from model 1, 0.15 from model 2 and 0.18 from model 3. The maximum log likelihood of the model 2 and 3 were higher than those of model 1 for all traits. In model 2 that raising farm was included as a random effect, the ratio of farm variance to the total phenotypic variance were ranged from 4% (EMA) to 18% (CWT). Top 10% and bottom 10% of female cows were selected based on the breeding values from model 1, and the Spearman's rank correlation coefficients among models were estimated for each trait within selected group. The correlation coefficients were ranged from 0.57 to 0.95 in top 10% group and from 0.68 to 0.95 in bottom 10% group. These results show that the discrepancies in the rankings of breeding values can be based on the models applied. In conclusion, the results obtained in this study suggest that the herd effect or farm effect should be included in the analytical model when breeding values are estimated with the purpose of improvement of carcass traits of Hanwoo breeding cows.

• Progress in Medical Physics
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• v.23 no.4
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• pp.309-316
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• 2012

Multimodal-imaging technique has been rapidly developed for improvement of diagnosis and evaluation of therapeutic effects. In despite of integrated hardware, registration accuracy was decreased due to a discrepancy between multimodal image and insufficiency of count in accordance with different acquisition method of each modality. The purpose of this study was to improve the PET image by event data resampling through analysis of data format, noise and statistical properties of small animal PET list data. Inveon PET listmode data was acquired as static data for 10 min after 60 min of 37 MBq/0.1 ml $^{18}F$-FDG injection via tail vein. Listmode data format was consist of packet containing 48 bit in which divided 8 bit header and 40 bit payload space. Realigned sinogram was generated from resampled event data of original listmode by using adjustment of LOR location, simple event magnification and nonparametric bootstrap. Sinogram was reconstructed for imaging using OSEM 2D algorithm with 16 subset and 4 iterations. Prompt coincidence was 13,940,707 count measured from PET data header and 13,936,687 count measured from analysis of list-event data. In simple event magnification of PET data, maximum was improved from 1.336 to 1.743, but noise was also increased. Resampling efficiency of PET data was assessed from de-noised and improved image by shift operation of payload value of sequential packet. Bootstrap resampling technique provides the PET image which noise and statistical properties was improved. List-event data resampling method would be aid to improve registration accuracy and early diagnosis efficiency.