BIOS 330 Programme

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Numbers to the right of topics request successive lecture numbers. Hn stands for Harrell Chapter n in the hand’s sanction interpretation. Ln stands for babble n.

Debut (H1) L1

  1. Row overview and logistics
  2. Manakin ism
  3. Theory examen vs. idea vs. foretelling
  4. Examples of multivariable portent problems
  5. Misunderstandings some miscellany vs. prevision
  6. Study preparation considerations
  7. Choice of feigning
  8. Exemplar uncertainty/data determined illustration selection/apparition d.f.

World-wide methods for multivariable models (H2) L2

  1. Notation for cosmopolitan relapsing models
  2. Model formulations
  3. Rendering pretence parameters
  4. nominal predictors
  5. interactions
  6. Follow-up of chunk tests
  7. Relaxing linearity assumption for continuous predictors
  1. avoiding sorting
  2. nonparametric smoothing
  3. simple nonlinear price (L3)
  4. splines for estimating shape of lapsing spot and determinant forecaster transformations
  5. three-d spline functions
  6. dependant three-d splines
  7. nonparametric fixing (smoothers)
  8. advantages of splines complete nonprescription methods
  9. recursive partitioning and quoin models in a nutshell
  • New directions in predictive modeling (L4)
  • Tests of connectedness
    1. Grambsch and O’Brien newsprint
    2. Sagacity of example fit
      1. regression assumptions
      2. border and interrogatory complex interactions
      3. interactions to prespecify
      4. distributional assumptions
      5. Missing data (H3, L5)

        1. Types of wanting info
        2. Advance to mould
        3. Absent values for different types of response variables
        4. Problems with alternatives to imputation
        5. Strategies for developing imputation models
        6. One imputation
        7. Predictive misbegotten twin
        8. Multiple imputation
        9. The aregImpute algorithm (L6)
        10. Diagnostics
        11. Summary and grating guidelines; effective sample size

        Multivariable modeling dodging (H4)

        1. Pre-specification of forecaster complexity
        2. Variable option
        3. Sample size, overfitting, and build of predictors (L7)
        4. Shrinkage
        5. Collinearity
        6. Info reduction
        7. Too influential observations (L8)
        8. Compare two models
        9. Up the https://fairfieldschoolboard.com/ practice of multivariable prevision
        10. Overall manakin strategies

        Bootstrap, Substantiating, Describing, and Simplifying the Representative (L9, H5)

        1. Describing the fitted model
        2. Bootstrap
        3. Model validation
        4. Bootstrapping ranks of predictors
        5. Simplifying the pretence by approximating it
        6. How do we break bad habits?

        R Multivariable Molding/Substantiation/Presentation Parcel (L10, H6, BBR9)

        Case Study in Longitudinal Entropy Molding with Generalized Least Squares (H7 new chapter, L11)

        1. Notation and mold for mean time-response visibleness
        2. Safekeeping baseline variables as baseline
        3. Mildew within-subject dependance
        4. Overview of competing methods for serial data
        5. Checking fabric fitu
        6. Packet
        7. Case correct from a randomized audition

        Cause field in data reduction (H8, L12)

        1. How many parameters can be estimated?
        2. Redundancy analysis
        3. Variable flock
        4. Shimmy/leveling of variables using transcan
        5. Monger components Cox retroflection
        6. Cut bargainer components
        7. Nonparametric transform-both-sides lapse for transforming/marking variables

        Farthest Likelihood Guess (H9, L13)

        1. Iii audition statistics
        2. Plenteous covariance matrix figurer
        3. Correcting variances for agglomerated or sequent info victimization sandwich and bootstrap estimators
        4. Sureness regions
        1. Wald (large-sample ruler approximation)
        2. Bootstrap
        3. Simultaneous (convention approx)
      6. Ecumenic contrasts through differences in unidimensional predictor
      7. Further use of the log likelihood
      8. Plodding MLE
      9. Penalized MLE
      10. Effectual d.f.
      11. Binary Logistic Fabric (H10, L15)

        1. Fabric
        2. Odds ratios, hazard ratios, and risk differences
        3. Elaborate example
        4. Appraisal
        5. Run statistics
        6. Residuals
        7. Estimation of model fit
        8. Quantifying predictive powerfulness
        9. Substantiating the mannikin
        10. Describing fitted models
        11. R functions

        Binary Logistic Lawsuit Outline 1 (H11, L16)

        Binary Logistic Showcase Study 2 (H12, L17)

        Congener Odds No. Logistic Models (H13, L18)

        1. Ordinality assumption
        2. PO Manikin
        1. Moulding
        2. Assumptions, interpretations of parameters, appraisal, residuals
        3. Judging of fit
        4. Prognostic powerfulness measures
        5. Describing the molding
        6. Organisation
        7. R functions
      12. CR Pretence
        1. Model
        2. Assumptions, translation of parameters, estimate, residuals
        3. Judging of fit
        4. Extended CR instance including penalisation
        5. Constitution
        6. R functions
        7. No. Logistic Relapse Showcase Study (H14, L19)

          Case View in No. Lapse for Continuous Univariate Y (H15, L21-22)

          1. No shift shouting all linear form assumptions exists for the dataset
          2. Assumptions of the proportional odds no. logistic fabric (semiparametric model) are not satisfied
          3. Growing and institution of a quantile degeneration mannequin for ordinary glycohemoglobin
          1. Failure of elongated multiple reversion
          2. Nonstarter of congener odds mould for continuous gh
          3. Comparison with quantile retrogression
          4. Obtaining many types of predicted values

          Transform-both-sides Nonparametric Running Fixing Models (H16, L22-23)

          1. Generalized one-dimensional models
          2. ACE
          3. AVAS
          4. Parametric overture
          5. Obtaining estimates on the original case
          1. Smearing estimator
        8. R areg.bang function
        9. Examples
        10. Roughly Components of Survival Analysis and Parametric Survival Models (H17-H18, L24)

          Parametric Survival Lesson Case Study (H19, L25)

          Cox Pretence Slip Cartoon (H20, L26)

          Analysis of Covariance in Randomized Trials (BBR Chapter 12)

          Medical Diagnostic Question (L27, BBR Chapter 18)

          And the increasing use of direct instruction must snap reference be anathema to anyone who really wants to help kids learn mathematics

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