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| author | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-10-14 12:51:39 -0400 |
|---|---|---|
| committer | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-10-14 12:51:39 -0400 |
| commit | 0841cb8a8d24c13d2579e494699409f6be6b98cc (patch) | |
| tree | 352c3dcbd38e07d3481ae6fef8fd21c234e5953f /finale/project_proposal.tex | |
| parent | 4fa0f3257276a515133bd8bc07e575f2b81d8fb5 (diff) | |
| download | cascades-0841cb8a8d24c13d2579e494699409f6be6b98cc.tar.gz | |
adding questions to objective
Diffstat (limited to 'finale/project_proposal.tex')
| -rw-r--r-- | finale/project_proposal.tex | 16 |
1 files changed, 15 insertions, 1 deletions
diff --git a/finale/project_proposal.tex b/finale/project_proposal.tex index c92bb36..912b281 100644 --- a/finale/project_proposal.tex +++ b/finale/project_proposal.tex @@ -1,4 +1,4 @@ -\documentclass[10pt]{article} +\documentclass[8pt]{article} \usepackage{fullpage, amsmath, amssymb, amsthm} \title{Regression Analysis with Network data} @@ -64,4 +64,18 @@ $$ \subsection*{Objectives} +\begin{itemize} +\item Try a Bayesian approach to estimate these parameters. Use the posterior +predictive distribution to obtain confidence intervals for the edge parameters. +Validate this with bootstrapping. How does this perform in different networks? +Can you intuitively link certain node-level/graph-level properties with the +resulting variance on the estimated parameter? +\item Do the previous observations correspond with the theoretical result, given +by the Fisher information matrix: $$\hat \beta \sim \mathcal{N}(\beta, +I{(\theta)}^{-1})$$ where $I(\theta) = - \left(\frac{\partial^2\log +\mathcal{L}}{\partial \theta^2} \right)^{-1}$ +\item Are there networks in which the Fisher information matrix is singular? +What happens to the estimation of $\beta$ in this case? +\end{itemize} + \end{document} |
