Regression

Results: 13118



#Item
151

Regression Rank: Learning to Meet the Opportunity of Descriptive Queries Matthew Lease1 , James Allan2, and Bruce Croft2 1 Brown Laboratory for Linguistic Information Processing (BLLIP)

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Source URL: bllip.cs.brown.edu

- Date: 2009-01-05 17:03:57
    152

    MODERN REGRESSION Fall 2012 Instructor: Rebecca Nugent Baker Hall 232C

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    Source URL: www.stat.cmu.edu

    - Date: 2012-11-19 14:54:54
      153

      Mixed Regression: Minimax Optimal Rates Constantine Caramanis The University of Texas at Austin Joint work with Yudong Chen and Xinyang Yi

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      Source URL: mmds-data.org

      - Date: 2014-06-20 15:26:10
        154

        Multicollinearity and Model Misspecification Christopher Winship, Bruce Western Harvard University Abstract: Multicollinearity in linear regression is typically thought of as a problem of large standard errors due to nea

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        Source URL: www.sociologicalscience.com

          155

          OR Spektrum: 525–543 c Springer-Verlag 2000  Equity index replication with standard and robust regression estimators

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          Source URL: www.wiwi.uni-passau.de

          - Date: 2010-04-05 09:26:35
            156

            Chart of the day Gold & Oil – buy the dips! Gold and oil are in a positive trend. Buy dips. Regression channel

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            Source URL: www.gsbanque.ch

            - Date: 2016-07-20 04:56:08
              157

              Regression analysis Lars Nesheim Centre for Microdata Methods and Practice (CeMMAP), UCL November 2011

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              Source URL: www.ifs.org.uk

              - Date: 2012-03-26 11:23:49
                158

                A prior near-ignorance Gaussian Process model for nonparametric regression Francesca Mangili Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola universitaria professionale della Svizzera italia

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                Source URL: ipg.idsia.ch

                - Date: 2016-07-18 04:25:32
                  159

                  149 DEVELOPMENT OF A NEW META-SCORE FOR PROTEIN STRUCTURE PREDICTION FROM SEVEN ALL-ATOM DISTANCE DEPENDENT POTENTIALS USING SUPPORT VECTOR REGRESSION MATSUYUKI SHIROTA1,2

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                  Source URL: www.jsbi.org

                  - Date: 2009-12-21 02:00:37
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