• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.125-136
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• 2011

Ground-level ozone, an air pollutant that is monitored by the Environmental Protection Agency (EPA), damages human health by irritating the respiratory system, reducing lung function, damaging lung cells, and aggravating asthma and other chronic conditions. In March 2008, the EPA strengthened ozone standards by lowering acceptable limits from 84 parts per billion to 75 parts per billion. Here epidemiologic data is used to study the effects of ozone regulation on human health and assessed how various regulatory standards for ozone may affect nonaccidental mortality, including respiratory-related deaths during ozone season. The assessment uses statistical methods based on hierarchical Bayesian models to predict the potential effects of the different regulatory standards. It also analyzes the variability of the results and ho they are impacted by different modeling assumptions. We focused on the technical an statistical approach to assessing relationship between new ozone regulations and mortality while other researches have detailed the relationship between ozone and human mortality. We shows a statistical correlation between ozone regulations and mortality, with lower limits of acceptable ozone linked to a decrease in deaths, and projects that mortality is expected to decrease by reducing ozone regulatory standards.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.155-168
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• 2000

Regarding to multiple comparison problem (MCP) of k normal population variances, we suggest a Bayesian method for calculating posterior probabilities for various hypotheses of equality among population variances. This leads to a simple method for obtaining pairwise comparisons of variances in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships among the variances. The method is derived from the fact that certain features of the hierarchical nonparametric family of Dirichlet process priors, in general, make it amenable to solving the MCP and estimating the posterior probabilities by means of posterior simulation, the Gibbs sampling. Two examples are illustrated for the method. For these examples, the method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.723-731
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• 1998

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and hazard rate function based on samples from a Burr type X failure model. Bayes calculations can be implemented by means of the Gibbs sampler and a numerical study us provided.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.145-153
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• 1998

In this paper, a hierarchical Bayesian analysis of a bivariate exponential model is discussed using Gibbs sampler. Parameters and reliability estimators are obtained. A numerical study is provided.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1621-1629
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• 2016

This paper studies Bayesian pooling for analysis of categorical data from small areas. Many surveys consist of categorical data collected on a contingency table in each area. Statistical inference for small areas requires considerable care because the subpopulation sample sizes are usually very small. Typically we use the hierarchical Bayesian model for pooling subpopulation data. However, the customary hierarchical Bayesian models may specify more exchangeability than warranted. We, therefore, investigate the effects of pooling in hierarchical Bayesian modeling for the contingency table from small areas. In specific, this paper focuses on the methods of direct or indirect pooling of categorical data collected on a contingency table in each area through Dirichlet priors. We compare the pooling effects of hierarchical Bayesian models by fitting the simulated data. The analysis is carried out using Markov chain Monte Carlo methods.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.163-168
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• 2002

전형적인 시공간모형은 시공간 변이도(semivariogram) 또는 공분산 함수(covariance function)를 필요로 한다. 본 논문에서는 계산하기 어렵고 현실적이지 못한 결합 공분산함수를 통한 고전적 모형 대신, 일련의 독립적인 조건분포를 이용하는 보다 현실적인 베이지안 계층모형을 이용한다. 미국 전 지역에 산재해 있는 138개 기온 관측소로부터 얻어진 61년(1920-1980) 동안의 연기온편차 자료에 시공간 베이지안 계층모형을 적용하고 순수시계열모형에서의 적합값과 제안된 모형의 적합값을 비교분석한다.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.5
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• pp.652-662
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• 2000

Most of established study about consumer behavior was directly connected abstract value with concrete purchase behavior, nevertheless several recognizable process is intervened between abstract concept and concept behavior. Of course researchers suggest hierarchical causality through means-end chain model. However empirical study is insufficient. And it's not certain whether the consumer's personal value affects actual evaluation about product attributes. Thus the purpose of this paper was to explore hierarchical causality of personal value, benefits sought and clothing product attributes and to suggest an alternative approach method. For the empircial study the data sets were collected through 150 female consumers living in Pusan and SAS and LISREL VIII were used for statistical analysis. As the result, hierarchical causality suggested by means-end chain model was positively substantiated. That is, benefits sought is differentiated according to personal value, and actual product attributes are indirectly influenced by personal value through benefits sought. Benefits sought are found to be key mediating variables.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.377-380
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• 2003

A new color image segmentation scheme is presented in this paper. The proposed algorithm consists of image simplification, region labeling and color clustering. The vector-valued diffusion process is performed in the perceptually uniform LUV color space. We present a discrete 3-D diffusion model for easy implementation. The statistical characteristics of each labeled region are employed to estimate the number of total clusters and agglomerative hierarchical clustering is performed with the estimated number of clusters. Since the proposed clustering algorithm counts each region as a unit, it does not generate oversegmentation along region boundaries.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.263-275
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• 2014

The present study used a hierarchical Bayesian approach was used to develop a mixed effect model to describe the transitional behavior of subjects in time nonhomogeneous Markov chains. The posterior distributions of model parameters were not in analytically tractable forms; subsequently, a Gibbs sampling method was used to draw samples from full conditional posterior distributions. The proposed model was implemented with real data.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.