• The Korean Journal of Applied Statistics
• /
• v.27 no.4
• /
• pp.605-617
• /
• 2014

Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.3
• /
• pp.315-320
• /
• 2002

Test-day records (n=13677) sampled from 896 ewes in 5-9 (${\mu}$=7.5) monthly test-days were used to estimate genetic and phenotypic parameters of test-day yields, lactation milk yield (TMY), length of the milking period (DAYS) and three measures of persistency of milk yield in Boutsico dairy sheep. Τhe measures of persistency were the slope of the regression line (${\beta}$), the coefficient of variation (CV) of the test-day milk yields and the maximum to average daily milk yield ratio (MA). The estimates of variance components were obtained under a linear mixed model by restricted maximum likelihood. The heritability of test-day yields ranged from 0.15 to 0.24. DAYS were found to be heritable ($h^2$=0.11). Heritability estimates of ${\beta}$, CV and MA were 0.15, 0.13, 0.10, respectively. Selection for maximum lactation yields is expected to result in prolonged milking periods, high rates of decline of yields after peak production, variable test-day yields and higher litter sizes. Selection for flatter lactation curves would reduce lactation yields, increase slightly the length of the milking period and decrease yield variation as well as litter size. The most accurate prediction of TMY was obtained with a linear regression model with the first five test-day records.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.6
• /
• pp.2917-2921
• /
• 2016

Background: To explore factors that influence quality of life (QOL) in patients receiving breast conserving therapy (BCT). Materials and Methods: In this sequential mixed methods study, 118 women from Songklanagarind Hospital were included. We used participants' characteristics, Body Image Scale (BIS), and Functional Assessment of Cancer Therapy with the Breast Cancer Subscale (FACT-B) for analysis. The BIS transformed into presence of body image disturbance (BID). Factors that influenced QOL were determined by stepwise multiple linear regression. Forty-one participants were selected for qualitative analysis. Our female researcher performed the semi-structured interviews with questions based on the symbolic interaction theory. Final codes were analysed using thematic analysis along with investigator triangulation methods. Results: Ninety percent had early stage breast cancer with post-completed BCT, for an average of 2.7 years. The median BIS score and FACT-B score were 2 (IQR=10) and 130 (IQR=39). In the regression analysis, an age of more than 50 years and BID were significant factors. As for the value of conserved breasts, two themes emerged: a conserved breast is an essential part of a participant's life and also the representation of her womanhood; the importance of a breast is related to age. Conclusions: Body image influenced QOL in post BCT participants. The conserved breasts also lead to positive and better impact on their body image as an essential part of their life.

• Earthquakes and Structures
• /
• v.3 no.1
• /
• pp.1-15
• /
• 2012

An empirical predictive relationship correlating bracketed duration to earthquake magnitude, site-to-source distance, and local site conditions (i.e. rock vs. stiff soil) for stable continental regions of North America is presented herein. The correlation was developed from data from 620 horizontal motions for central and eastern North America (CENA), consisting of 28 recorded motions and 592 scaled motions. The bracketed duration data was comprised of nonzero and zero durations. The non-linear mixed-effects regression technique was used to fit a predictive model to the nonzero duration data. To account for the zero duration data, logistic regression was conducted to model the probability of zero duration occurrences. Then, the probability models were applied as weighting functions to the NLME regression results. Comparing the bracketed durations for CENA motions with those from active shallow crustal regions (e.g. western North America: WNA), the motions in CENA have longer bracketed durations than those in the WNA. Especially for larger magnitudes at far distances, the bracketed durations in CENA tend to be significantly longer than those in WNA.

• Journal of the Korea Computer Graphics Society
• /
• v.26 no.5
• /
• pp.15-24
• /
• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Journal of Korean Medical Science
• /
• v.33 no.53
• /
• pp.322.1-322.14
• /
• 2018

Background: Osteoprotegerin (OPG) plays protective roles against the development of vascular calcification (VC) which greatly contributes to the increased cardiovascular events in patients with chronic kidney disease (CKD). The present study aimed to find the non-traditional, kidney-related cardiovascular risk factors correlated to serum OPG and the effect of serum OPG on the arterial stiffness measured by brachial ankle pulse wave velocity (baPWV) in patients with the pre-dialysis CKD. Methods: We cross-sectionally analyzed the data from the patients in whom baPWV and the serum OPG were measured at the time of enrollment in a prospective pre-dialysis CKD cohort study in Korea. Results: Along with traditional cardiovascular risk factors such as age, diabetes mellitus, pulse pressure, and baPWV, non-traditional, kidney-related factors such as albuminuria, plasma level of hemoglobin, total $CO_2$ content, alkaline phosphatase, and corrected calcium were independent variables for serum OPG in multivariate linear regression. Reciprocally, the serum OPG was positively associated with baPWV in multivariate linear regression. The baPWV in the 3rd and 4th quartile groups of serum OPG were higher than that in the 1st quartile group after adjustments by age, sex and other significant factors for baPWV in linear mixed model. Conclusion: Non-traditional, kidney-related cardiovascular risk factors in addition to traditional cardiovascular risk factors were related to serum level of OPG in CKD. Serum OPG level was significantly related to baPWV. Our study suggests that kidney-related factors involved in CKD-specific pathways for VC play a role in the increased secretion of OPG into circulation in patients with CKD.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.20 no.3
• /
• pp.181-194
• /
• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of Life Science
• /
• v.8 no.5
• /
• pp.504-508
• /
• 1998

A study was conducted to evaluate a protein removal characteristics by foam separation. The foam separator was operated in well-mixed tank which would be considered as a completely mixed condition. The feasibility of foam separation to remove protein from fresh and sea water was investigated. Protein removal characteristics of the foam separator were obtained by batch reactor operations. To find the effect of the operating parameter to protein removal rate, the foam separation was carried with variation of initial protein concentration and foam height. The results indicated that the protein removal efficiency was increased with increasing protein concentration and decreased with increasing foam height. The relationship between protein concentration and protein removal rate was evaluated by linear regression.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.987-998
• /
• 2014

The primary purpose of this paper is to derive a benchmark dose lower limit (BMDL) of lead for the attention deficit/hyperactive disorder (ADHD) based on a longitudinal cohort data set which is referred to as CHEER data set. The CHEER data were recently recruited from the Ministry of Environment of S. Korea to investigate the effect of environment on children's health We first confirm the correlation of ADHD with the blood lead level using a linear mixed effect model. We report from the longitudinal characteristic of CHEER data that ADHD scores tend to have "regression to the mean". A dose-response curve of blood lead level with ADHD being the end point is derived and from this dose-response curve a few BMDLs are derived based on corresponding assumptions on the benchmark region.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.41-49
• /
• 2019

We are to build an unsupervised machine learning-based language model which can estimate the amount of information that are in need to process words consisting of subword-level morphemes and syllables. We are then to investigate whether the reading times of words reflecting their morphemic and syllabic structures are predicted by an information-theoretic measure such as surprisal. Specifically, the proposed Morfessor-based unsupervised machine learning model is first to be trained on the large dataset of sentences on Sejong Corpus and is then to be applied to estimate the information-theoretic measure on each word in the test data of Korean words. The reading times of the words in the test data are to be recruited from Korean Lexicon Project (KLP) Database. A comparison between the information-theoretic measures of the words in point and the corresponding reading times by using a linear mixed effect model reveals a reliable correlation between surprisal and reading time. We conclude that surprisal is positively related to the processing effort (i.e. reading time), confirming the surprisal hypothesis.