• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.775-791
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• 2017

We frequently encounter binary data in real life. Logistic, Probit, Cauchit, Complementary log-log models are often used for binary data analysis. In order to analyze binary data, Liu (2004) proposed a Robit model, in which the inverse of cdf of the Student's t distribution is used as a link function. Kim et al. (2008) also proposed a generalized t-link model to make the binary regression model more flexible. The more flexible skewed distributions allow more flexible link functions in generalized linear models. In the sense, we propose a binary data regression model using skewed generalized t distributions introduced in Theodossiou (1998). We implement R code of the proposed models using the glm function included in R base and R sgt package. We also analyze Pima Indian data using the proposed model in R.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.285-299
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• 2020

In many cases, we are interested in identifying independence between variables. For continuous random variables, correlation coefficients are often used to describe the relationship between variables; however, correlation does not imply independence. For finite discrete random variables, we can use the Pearson chi-square test to find independency. For the mixed type of continuous and discrete random variables, we do not have a general type of independent test. In this study, we develop a independence test of a continuous random variable and a discrete random variable without assuming a specific distribution using kernel density estimation. We provide some statistical criteria to test independence under some special settings and apply the proposed independence test to Pima Indian diabetes data. Through simulations, we calculate false positive rates and true positive rates to compare the proposed test and Kolmogorov-Smirnov test.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.47-53
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• 2019

The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model's learning are from three main factors: variance, noise, and bias. By using ensemble methods, we're able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we're able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest.

• Biomedical Science Letters
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• v.15 no.4
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• pp.301-307
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• 2009

Whole-body insulin resistance results largely from impaired insulin-stimulated glucose disposal in skeletal muscle. Our previous studies using differential display and quantitative real-time RT-PCR have shown that a novel cDNA band (DD23) had a higher level of expression in insulin resistant skeletal muscle and it was correlated with whole-body insulin action, independent of age, sex, and percent body fat. In this study, we cloned and characterized DD23. The DD23 sequence is part of the 3'UTR region of the RNA binding motif, single stranded interacting protein (RBMS3). We have cloned the full length cDNA for RBMS3 and identified two splice variants. These variants named DD23-L and DD23-S have 15 and 14 exons respectively and differ from RBMS3 in the 3'UTR significantly. Northern blot analyses showed that an ~8.8 kb mRNA transcript of DD23 was predominantly expressed in skeletal muscle and to a lesser extent in placenta, but not in heart, brain, lung, liver, or kidney, unlike RBMS3. Elevated expression levels of these novel alternatively spliced variants of RBMS3 in skeletal muscle may play a role in whole body insulin resistance.