• Journal of Applied Reliability
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• v.1 no.2
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• pp.149-163
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• 2001

We study the normality of the maximum partial likelihood estimators for the proportional hazard model with informative censored data. The proposed models cover the cases in which the times to a primary event may be informatively or randomly censored and the times to a secondary event may be randomly censored. To estimate the parameters and to check the normality of the parameters in the model, we adopt the partial likelihood and counting process to use the martingale central limit theorem. Simulation studies are performed to examine the normality of the MPLE's for the five cases in which they depend upon the proportions of randomly censored and informative censored data.

• Iberoamérica
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• v.14 no.2
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• pp.119-145
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• 2012

This paper deals with the study of how the 'birth' of Brazil happened and how the European utopianism was represented in Brazilian Informative Literature of the sixteenth century, that is, the first literary manifestations of Brazil. There were at that time the Renaissance humanism and the scientific development that encouraged dozens of illustrious navigators for new discoveries ultra-seas, like Christopher Columbus who discovered America. Writings such as Letter to King Manuel of Pero Vaz de Caminha, had mostly an intention to inform Europeans about climates, indians and possibility to discover gold or silver. Main narrative characteristics were uncertainty and exageration, which ironically helped to attract more discoveries and explorations in the New World. Americo Vespucio's Mundus Novus inspired Thomas More to write Utopia, in which the author described through a Portuguese sailor the ideal but unrealizable society. Utopianism regarding the imaginary island of 'Brazi', well known among Europeans since long ago, may have influenced the current name of the country: 'Brazil'. On the other hand, utopianism shown in Brazilian Informative Literature worked as a justification for Europeans to explore and colonize the New World.

• (The) Korean Journal of Advertising
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• v.25 no.5
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• pp.7-42
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• 2014

This study is designed to examine the effects of information source's social distance on message attitude and online word-of-mouth intention (e-WOM). It also examined the moderation effects of blatant persuasive intention of message, the normative interpersonal influences, and the informative interpersonal influences on the relationship between social distance and advertising effectiveness. This study employed an experiment: 2(far/near social distance far/near) ${\times}2$(high/low blatant persuasive intention of message) ${\times}$(high/low normative interpersonal influences) ${\times}2$(high/low informative interpersonal influences). The results of this study are as follows. First, closer social distance led to more positive message attitude and higher online word-of-mouth intention. Second, when blatant persuasive intention of message is low, the effects of social distance on message attitude and WOM intention were more noticeable while those effects were less significant for high blatant persuasive intention of message. Third, there were no interaction effects of social distance and normative interpersonal influences on advertising effectiveness. Fourth, the effects of social distance on message attitude and WOM intention were more significant for high informative interpersonal influences than for low informative interpersonal influences. Implications of study findings are provided for strategic use of corporate Facebook page to generate positive consumer responses.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.331-343
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• 2014

We propose two estimating procedures to analyze clustered interval-censored data with an informative cluster size based on a marginal model and investigate their asymptotic properties. One is an extension of Cong et al. (2007) to interval-censored data and the other uses the within-cluster resampling method proposed by Hoffman et al. (2001). Simulation results imply that the proposed estimators have a better performance in terms of bias and coverage rate of true value than an estimator with no adjustment of informative cluster size when the cluster size is related with survival time. Finally, they are applied to lymphatic filariasis data adopted from Williamson et al. (2008).

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.714-719
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• 2005

In this paper, the informative space is proposed to implant ubiquitous functions into physical spaces. We merge physical and virtual spaces through the space structurization using an RFID system, and solve the space localization and mapping problem for a robot to navigate through the distribution and synthesis of information and knowledge. To distribute knowledge flexibly and reliably to changing environment and also to develop a system which allows a robot to invoke and merge the distributed knowledge more freely, we employ a novel approach of knowledge management based on Web services. The proposed method is verified by building a physical space with two kinds of RFID tags and a virtual space with knowledge database based on Web services.

• Asia-Pacific Journal of Business
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• v.8 no.1
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• pp.15-32
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• 2017

This study was designed to examine the antecedents of mobile app usage among smart phone users. An extended TAM, which included the additional factors of perceived enjoyment, perceived informative usefulness and perceived social usefulness, was applied to predict people's intention to use mobile apps. Overall, the hypothesized research model did a fairly good job explaining significant associations between the independent variables and the dependent variable. The findings had showed that perceived social usefulness, perceived enjoyment and attitude were significantly affect intention to use mobile apps. Meanwhile, perceived ease of use (PEOU) and perceived informative usefulness were not significantly effects attitude towards intention to use mobile apps. Therefore, mobile apps developers should develop mobile apps that are easier for the users to seek information. For the information available should be more precise and bringing more benefits to the users.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.188-196
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• 2017

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.

• Genomics & Informatics
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• v.2 no.1
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• pp.19-29
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• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.