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  <Article>
    <Journal>
      <PublisherName>岡山大学環境理工学部</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2187-6940</Issn>
      <Volume>19</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2014</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>影響関数による近似の数値評価</ArticleTitle>
    <FirstPage LZero="delete">1</FirstPage>
    <LastPage>7</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Hong Mei</FirstName>
        <LastName>Bao</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kaoru</FirstName>
        <LastName>Fueda</LastName>
        <Affiliation/>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi">10.18926/fest/52227</ArticleId>
    </ArticleIdList>
    <Abstract>For evaluating statistical models one of the most effective criteria is cross-validation. But it
requires a large amount of computation. Various alternative schemes are considered to reduce
its computation. Modified generalized information criterion is one of those alternative schemes.
In this criterion an influence function is used to estimate the parameters of the models. By the
numerical simulation we studied the effect of an influence function.　
Surveying data of the lake depth are used as the sample data. We estimate the shape of
lake bottom as spline surface. The estimated parameters and the estimated depths obtained
by two criteria are compared and the effect of an information function is analysed.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">influence function</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">information criteria</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">CV</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">mGIC</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">B-spline</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>岡山大学環境理工学部</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2185-3347</Issn>
      <Volume>16</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2011</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>ドルコスト平均法を用いた投資の有効性の検証</ArticleTitle>
    <FirstPage LZero="delete">1</FirstPage>
    <LastPage>5</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Masayuki</FirstName>
        <LastName>Touji</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kaoru</FirstName>
        <LastName>Fueda</LastName>
        <Affiliation/>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi">10.18926/fest/44790</ArticleId>
    </ArticleIdList>
    <Abstract>As a method of long term investment for private investor, the dollar cost averaging investment
method is well known and seems to reduce the purchase cost because we purchase risk assets with
same amount of monye every month, then we purchase many assets when the price of assets is low and few assets when the price is high. On the other hand, if the expectation of the return of the risk assets is positive, we have the maximum expectation of return when we purchase the risk assets with all of money to invest. To reduce the risk of investment, diversified investments are effective. However question whehter we use the dollar cost averaging investment method or investe money all at once to well-diversified
risk assets remains. In this study, we validate the effecte of the ddollar cost averaging investment method by Monte Carlo simulation.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Dollar cost averaging</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">investment</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Monte Carlo simulation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Stock price index</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>岡山大学環境理工学部</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1341-9099</Issn>
      <Volume>13</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2008</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>石垣島におけるマラリア流行解析のための統計モデリング</ArticleTitle>
    <FirstPage LZero="delete">7</FirstPage>
    <LastPage>15</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Masao</FirstName>
        <LastName>Ueki</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yuuki</FirstName>
        <LastName>Nakagawa</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kaoru</FirstName>
        <LastName>Fueda</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Hirofumi</FirstName>
        <LastName>Ishikawa</LastName>
        <Affiliation/>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi">10.18926/fest/12816</ArticleId>
    </ArticleIdList>
    <Abstract>It is necessary to consider a stochastic variability in modeling malaria epidemic behavior since the　malaria infection  cycle essentially depends on stochastic elements. For this requirement, we need to construct an appropriate statistical model from available data in advance. In this report, we provide some statistical models for the analysis of malaria epidemic behavior at Ishigaki Island. These models can be used for recurrence of past malaria epidemic and prediction of future malaria epidemic at Ishigaki Island.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Anopheles minimus,</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">generalized liner model</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Ishigaki Island</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">malaria epidemic behavior</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">prediction</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">stochastic model</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>岡山大学環境理工学部</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1341-9099</Issn>
      <Volume>12</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2007</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>合成変数の推定を利用した項目選択とその数値的検討</ArticleTitle>
    <FirstPage LZero="delete">29</FirstPage>
    <LastPage>40</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yuichi</FirstName>
        <LastName>Mori</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kaoru</FirstName>
        <LastName>Fueda</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Masaya</FirstName>
        <LastName>Iizuka</LastName>
        <Affiliation/>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi">10.18926/fest/11429</ArticleId>
    </ArticleIdList>
    <Abstract>A variable selection method using global score estimation is proposed, which is applicable as a selection criterion in any multivariate method without external variables such as principal component analysis. This method selects a reasonable subset of variables so that the global scores, e.g. principal component scores, which are computed based on the selected variables, approximate the original global scores as well as possible in the context of the least squares. Three computational steps are proposed to estimate the scores according to how to satisfy the restriction that the estimated global scores are mutually uncorrelated. Three different examples are analyzed to demonstrate the performance and usefulness of the proposed method numerically, in which three steps are evaluated and the results obtained using four cost-saving selection procedures are compared.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">principal components</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">least square</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">orthogonalization</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">cost-saving selection</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>岡山大学環境理工学部</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1341-9099</Issn>
      <Volume>11</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2006</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>社会的責任投資の視点から評価した日本企業の環境対策</ArticleTitle>
    <FirstPage LZero="delete">31</FirstPage>
    <LastPage>41</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yousuke</FirstName>
        <LastName>Ikeda</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kaoru</FirstName>
        <LastName>Fueda</LastName>
        <Affiliation/>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi">10.18926/fest/11428</ArticleId>
    </ArticleIdList>
    <Abstract>Recently, Socially Responsible Investment, which is a policy of investment regarding with companies' social, environmental and moral value, attracts attention of investors. However, there are little data to explain the effectiveness of the SIR in Japan. In this paper we report the relation between environmental management and stock price of Japanese companies.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Socially Responsible Investment</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Environmental rating</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Eco fund</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>岡山大学環境理工学部</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1341-9099</Issn>
      <Volume>11</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2006</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>区間上のデータに対するカーネル密度推定法</ArticleTitle>
    <FirstPage LZero="delete">27</FirstPage>
    <LastPage>30</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Masao</FirstName>
        <LastName>Ueki</LastName>
        <Affiliation/>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kaoru</FirstName>
        <LastName>Fueda</LastName>
        <Affiliation/>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi">10.18926/fest/11427</ArticleId>
    </ArticleIdList>
    <Abstract>In the field of data analysis, including environmental data, it is important to know the shape of underlying density function. In this case, we often use histogram which provides an information about the board line of density's curve. However histogram can not be the best method when the true density function is continuous, as is often the cases. On the other hand, kernel density estimator is another popular one which gives a continuous function. In some practical cases, however, there is a case that some knowledges about the range of the data are previously given. For instance, data of percentage, such as mortality rate, only takes the values on [0,1]. This paper considers two different modifications in kernel density estimator for the data on known interval and compares them.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">kernel density estimator</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">adaptive bandwidth</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">data on finite interval</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
</ArticleSet>
