\'Linear Hierarchical Models\'

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# 'Linear Hierarchical Models' - PowerPoint PPT Presentation

\'Linear Hierarchical Models\'. Definition Computation Applications. ……the girlie way. Definition. Hierarchical models provide a “framework” for both Classical inference (at first &amp; second level) &amp; Parametric Empirical Bayes (PEB) at first level (constrained by higher levels).

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Presentation Transcript

### \'Linear Hierarchical Models\'

Definition

Computation

Applications

……the girlie way

Definition

Hierarchical models provide a “framework” for both

Classical inference (at first & second level) &

Parametric Empirical Bayes (PEB) at first level (constrained by higher levels).

Extension of the general linear model but with more variance components.

Computation

Both Classical and Bayesian approaches rest on

estimating the covariance components

Covariance components refer to the multiple variance components in the data

(including within & between subject variance)

Covariance components estimation is based on:

Maximum Likelihood estimator (ML) or the Expectation Maximization (EM) algorithm

= ML plus expectation step.

y = response variable;

• X = design matrix/explanatory variables;
• = parameters (betas) e = error

1st Level: Activation over scans

(within session & subject)

2nd Level: Activation over sessions

(within subjects)

3rd Level: Subject specific effects

(over subjects)

Parameters (q ): Quantities that determine the expected response (the effect)

Can be estimated from mean without knowing its variance

Hyperparameters: Variance of these quantities (within & between subject)

(classically estimated using residual sum of squares)

Classical approach:

how large are parameters with respect to standard error

inference usually made at highest level

(using between subject variability).

Bayesian inference:

probability of the parameters given the data

prior covariance = error covariance at level above

i.e. inference at lower levels (constrained by higher levels)

e.g. Estimate of single subject can be constrained by knowledge of population using

between subject variability from 2nd level as priors for parameters at first level.

SPM uses activation over voxels (within subject) as the priors

E.g. Within subject

1st Level: Activation over scans

2nd Level: Activation over voxels

Both Classical and Bayesian approaches rest on estimating the

covariance components

Covariance component estimation

= estimating hyperparameters (within & between subject contributions to error)

(hierarchically organised & linearly mixed variance components)

Flattened to:

Variance components are estimated with:

Expectation Maximization (EM) Algorithm

ExpectationMaximization (EM) Algorithm

EM estimates both parameters and hyperparameters from the data

(using generic iterative / re-estimation procedure)

E. step: finds expectation & covariance of the parameters

holding hyperparameters fixed.

M. step: updates maximum likelihood estimate of hyperparameters

holding parameters fixed.

EM used when hyperparameters of the likelihood and prior densities are not known.

these hyperparameters become variance components that can be

estimated using restricted maximum likelihood (ReML)

ReML involves M step but not E step

finds unknown variance components without explicit reference to the parameters.

……over to Raj