• Title/Summary/Keyword: Statistical Information

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Contents Analysis on the Internet Sites for Statistical Information

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.04a
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    • pp.131-140
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    • 2006
  • There are many statistical information sites as the use of internet is increased quickly in recent years. In this paper, we explore and analyze internet sites for statistical information such as statistical survey system, education, database, and terminology. And then we classify these sites to apply statistical information to some particular spheres easily. In so doing, this study result aims at enhancing our understanding of internet sites for statistical information.

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Contents and Patent Map Analysis on the Internet Sites for Statistical Information

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.411-420
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    • 2006
  • There are many statistical information sites as the use of internet is increased quickly in recent years. In this paper, we explore and analyze internet sites for statistical information such as statistical survey system, education, database, and terminology. And then we classify these sites to apply statistical information to some particular spheres easily. Also, we analyze the patent map for domestic patents of statistical information. In so doing, the result of this study aims at enhancing our understanding of internet sites for statistical information.

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A Data Mining Approach for a Dynamic Development of an Ontology-Based Statistical Information System

  • Mohamed Hachem Kermani;Zizette Boufaida;Amel Lina Bensabbane;Besma Bourezg
    • Journal of Information Science Theory and Practice
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    • v.11 no.2
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    • pp.67-81
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    • 2023
  • This paper presents a dynamic development of an ontology-based statistical information system supporting the collection, storage, processing, analysis, and the presentation of statistical knowledge at the national scale. To accomplish this, we propose a data mining technique to dynamically collect data relating to citizens from publicly available data sources; the collected data will then be structured, classified, categorized, and integrated into an ontology. Moreover, an intelligent platform is proposed in order to generate quantitative and qualitative statistical information based on the knowledge stored in the ontology. The main aims of our proposed system are to digitize administrative tasks and to provide reliable statistical information to governmental, economic, and social actors. The authorities will use the ontology-based statistical information system for strategic decision-making as it easily collects, produces, analyzes, and provides both quantitative and qualitative knowledge that will help to improve the administration and management of national political, social, and economic life.

A Statistical Model for Choosing the Best Translation of Prepositions. (통계 정보를 이용한 전치사 최적 번역어 결정 모델)

  • 심광섭
    • Language and Information
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    • v.8 no.1
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    • pp.101-116
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    • 2004
  • This paper proposes a statistical model for the translation of prepositions in English-Korean machine translation. In the proposed model, statistical information acquired from unlabeled Korean corpora is used to choose the best translation from several possible translations. Such information includes functional word-verb co-occurrence information, functional word-verb distance information, and noun-postposition co-occurrence information. The model was evaluated with 443 sentences, each of which has a prepositional phrase, and we attained 71.3% accuracy.

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The Role of Distributional Cues in the Acquisition of Verb Argument Structures

  • Kim, Mee-Sook
    • Language and Information
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    • v.7 no.1
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    • pp.87-99
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    • 2003
  • This paper investigates the role of input frequency in the acquisition of verb argument structures based on distributional information of a corpus of utterances derived from the English CHILDES database (MacWhinney 1993). It has been widely accepted that children successfully learn verb argument structures by innate language mechanisms, such as linking rules which connect verb meanings and its syntactic structures. In contrast, an approach to language acquisition called “statistical language learning” has currently claimed that children could succeed in acquiring syntactic structures in the absence of innate language mechanisms, making use of distributional properties of the input. In this paper, I evaluate the feasibility of the statistical learning in acquiring verb argument structures, based on distributional information about locative verbs in parental input. The naturalistic data allow us to investigate to what extent the statistical learning approach can and cannot help children succeed in learning the syntax of locative verbs. Based on the results of English database analysis, I show that there is rich statistical information for learning the syntactic possibilities of locative verbs in parental input, despite some limitations in the statistical learning approach.

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The Design and Implementation of Web-based Statistical Consulting System

  • Ryu, Jae-Yeol;Lee, Jung-Hoon;Jo, Min-Ji;Kim, Ae-Ji
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.11a
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    • pp.167-180
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    • 2006
  • The statistical survey and analysis is much restricted to time, space and material. The statistical survey and analysis could hardly resume. The statistical survey and analysis is very important to create various and accurate information. The statistical survey and analysis which is not a expert knowledge have many problems in productivity of information, reliability and etc. In this paper, we study the design and Implementation of web-based statistical survey and analysis consulting system which a client meet easily a statistical expert on the web.

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REGRESSION WITH CENSORED DATA BY LEAST SQUARES SUPPORT VECTOR MACHINE

  • Kim, Dae-Hak;Shim, Joo-Yong;Oh, Kwang-Sik
    • Journal of the Korean Statistical Society
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    • v.33 no.1
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    • pp.25-34
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    • 2004
  • In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

Study on Improving Oriental Medicine Statistical System for Multidimensional Statistical Data

  • Yea, Sang-Jun;Kim, Chul;Kim, Jin-Hyun;Jang, Hyun-Chul;Kim, Sang-Kyun;Song, Mi-Young
    • International Journal of Contents
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    • v.7 no.3
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    • pp.13-18
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    • 2011
  • Oriental medicine statistics are essential in research planning, research evaluation, and policy decision based on objective data. However, integrated administration of such statistics is not presently possible in the oriental medicine field, which has been slow in incorporating information communication technology. In an effort to address this problem, the Korea Institute of Oriental Medicine (KIOM) developed an oriental medicine statistical system in 2009, and the system has been offered in the traditional medicine information portal of OASIS. However, according to a 2010 survey targeting OASIS users, those surveys reported that needs for a system where various statistical data can be extracted via an interactive approach to multidimensional data. As a result of an analysis of the functions of the existing system, it was found that it is necessary to array and arithmetically analyze Stats Value, Drill Up & Drill Down, and Pivot. To this end, the existing DB schema should be redesigned. Based on our analysis result, we redesigned the database into a structure that is applicable to the reverse pivot algorithm. We used J2EE/JSP and a Flex framework to design and develop an oriental medicine statistical system that can provide multidimensional statistical data. Considering that the improved oriental medicine statistical system is planned to be offered by OASIS of KIOM, utilization and value of oriental medicine statistical data are expected to be enhanced.

Statistical micro matching using a multinomial logistic regression model for categorical data

  • Kim, Kangmin;Park, Mingue
    • Communications for Statistical Applications and Methods
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    • v.26 no.5
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    • pp.507-517
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    • 2019
  • Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.