• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.123-137
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• 2018

A nonlinear mixed effects model is mainly used to analyze repeated measurement data in various fields. A nonlinear mixed effects model consists of two stages: the first-stage individual-level model considers intra-individual variation and the second-stage population model considers inter-individual variation. The individual-level model, which is the first stage of the nonlinear mixed effects model, estimates the parameters of the nonlinear regression model. It is the same as the general nonlinear regression model, and usually estimates parameters using the least squares estimation method. However, the least squares estimation method may have a problem that the estimated value of the parameters and standard errors become extremely large if the assumed nonlinear function is not explicitly revealed by the data. In this paper, a new estimation method is proposed to solve this problem by introducing the ridge regression method recently proposed in the nonlinear regression model into the first-stage individual-level model of the nonlinear mixed effects model. The performance of the proposed estimator is compared with the performance with the standard estimator through a simulation study. The proposed methodology is also illustrated using quantitative high throughput screening data obtained from the US National Toxicology Program.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.

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

• Tuberculosis and Respiratory Diseases
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• v.58 no.3
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• pp.230-242
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• 2005

Background : Spirometry should be compared with the normal predictive values obtained from the same population using the same procedures, because different ethnicity and different procedures are known to influence the spirometry results. This study was performed to obtain the normal predictive values of the Forced Vital Capacity(FVC), Forced Expiratory Volume in 1 Second($FEV_1$), Forced Expiratory Volume in 6 Seconds($FEV_6$), and $FEV_1/FVC$ for a representative Korean population. Methods : Based on the 2000 Population Census of the National Statistical Office of Korea, stratified random sampling was carried out to obtain representative samples of the Korean population. This study was performed as a part of the National Health and Nutrition Survey of Korea in 2001. The lung function was measured using the standardized methods and protocols recommended by the American Thoracic Society. Among those 4,816 subjects who had performed spirometry performed, there was a total of 1,212 nonsmokers (206 males and 1,006 females) with no significant history of respiratory diseases and symptoms, with clear chest X-rays, and with no significant exposure to respiratory hazards subjects. Their residence and age distribution was representative of the whole nation. Mixed effect models were examined based on the Akaike's information criteria in statistical analysis, and those variables common to both genders were analyzed by regression analysis to obtain the final equations. Results : The variables affecting the normal predicted values of the FVC and $FEV_6$ for males and females were $age^2$, height, and weight. The variables affecting the normal predicted values of the $FEV_1$ for males and females were $age^2$, and height. The variables affecting the normal predicted values of the $FEV_1/FVC$ for male and female were age and height. Conclusion : The predicted values of the FVC and $FEV_1$ was higher in this study than in other Korean or foreign studies, even though the difference was < 10%. When compared with those predicted values for Caucasian populations, the study results were actually comparable or higher, which might be due to the stricter criteria of the normal population and the systemic quality controls applied to the whole study procedures together with the rapid physical growth of the younger generations in Korea.