John Cook

Results: 273



#Item
1Exact calculation of inequality probabilities John D. Cook Department of Biostatistics P. O. Box, Unit 1409 The University of Texas, M. D. Anderson Cancer Center Houston, Texas, USA

Exact calculation of inequality probabilities John D. Cook Department of Biostatistics P. O. Box, Unit 1409 The University of Texas, M. D. Anderson Cancer Center Houston, Texas, USA

Add to Reading List

Source URL: www.johndcook.com

Language: English - Date: 2015-01-27 08:09:55
    2Basic properties of the soft maximum John Cook Department of Biostatistics, Unit 1409 The University of Texas, M. D. Anderson Cancer Center Houston, Texas 77030, USA

    Basic properties of the soft maximum John Cook Department of Biostatistics, Unit 1409 The University of Texas, M. D. Anderson Cancer Center Houston, Texas 77030, USA

    Add to Reading List

    Source URL: www.johndcook.com

    Language: English - Date: 2015-01-27 08:11:06
      3One-arm binary predictive probability John D. Cook  September 9, 2011  Suppose θ is the probability of success in a Bernoulli trial and θ has a

      One-arm binary predictive probability John D. Cook September 9, 2011 Suppose θ is the probability of success in a Bernoulli trial and θ has a

      Add to Reading List

      Source URL: www.johndcook.com

      Language: English - Date: 2013-07-09 18:22:36
        42018 ICCTA Award Winners ADVOCACY AWARD Dr. John Avendano, Kankakee Community College BUSINESS/INDUSTRY PARTNERSHIP AWARD Cook Canton and Spoon River College CERTIFICATE OF MERIT

        2018 ICCTA Award Winners ADVOCACY AWARD Dr. John Avendano, Kankakee Community College BUSINESS/INDUSTRY PARTNERSHIP AWARD Cook Canton and Spoon River College CERTIFICATE OF MERIT

        Add to Reading List

        Source URL: www.communitycolleges.org

        Language: English - Date: 2018-06-02 12:03:46
          5Power and Bias in Adaptively Randomized Clinical Trials Technical Report UTMDABTRJ. Kyle Wathen John D. Cook Department of Biostatistics, Box 447

          Power and Bias in Adaptively Randomized Clinical Trials Technical Report UTMDABTRJ. Kyle Wathen John D. Cook Department of Biostatistics, Box 447

          Add to Reading List

          Source URL: www.johndcook.com

          Language: English - Date: 2013-07-09 18:23:21
            6Upper bounds on non-central chi-squared tails and truncated normal moments John D. Cook Department of Biostatistics P. O. Box, Unit 1409 The University of Texas, M. D. Anderson Cancer Center

            Upper bounds on non-central chi-squared tails and truncated normal moments John D. Cook Department of Biostatistics P. O. Box, Unit 1409 The University of Texas, M. D. Anderson Cancer Center

            Add to Reading List

            Source URL: www.johndcook.com

            Language: English - Date: 2015-01-27 08:10:29
              7Determining distribution parameters from quantiles John D. Cook Department of Biostatistics The University of Texas M. D. Anderson Cancer Center P. O. BoxUnit 1409

              Determining distribution parameters from quantiles John D. Cook Department of Biostatistics The University of Texas M. D. Anderson Cancer Center P. O. BoxUnit 1409

              Add to Reading List

              Source URL: www.johndcook.com

              Language: English - Date: 2013-07-09 18:23:26
                8Introduction to Stokes’ Equation John D. Cook September 8, 1992 Abstract These notes are based on Roger Temam’s book on the Navier-Stokes equations. They cover the well-posedness and regularity results for the statio

                Introduction to Stokes’ Equation John D. Cook September 8, 1992 Abstract These notes are based on Roger Temam’s book on the Navier-Stokes equations. They cover the well-posedness and regularity results for the statio

                Add to Reading List

                Source URL: www.johndcook.com

                Language: English - Date: 2013-07-09 18:22:13
                  9Predictive probabilities for normal outcomes John Cook September 15, 2011 Suppose Y ∼ normal(θ, σ 2 ) and a priori θ ∼ normal(µ, τ ). After observing y1 , y2 , . . . , yn the posterior distribution on θ is norm

                  Predictive probabilities for normal outcomes John Cook September 15, 2011 Suppose Y ∼ normal(θ, σ 2 ) and a priori θ ∼ normal(µ, τ ). After observing y1 , y2 , . . . , yn the posterior distribution on θ is norm

                  Add to Reading List

                  Source URL: www.johndcook.com

                  Language: English - Date: 2013-07-09 18:22:24