Last updated: 2018-06-03

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Project Overview

This project aims to study a generalization version of normal version of smash. It is a version of wavelet denoising for Gaussian where each observation has additional known measurement error. The method is expected to handle the Poisson nugget effect.

I have created a R package for the functions used in the analysis.

Analysis

Intial investigation

  1. Basic review
  2. Poisson nugget simulation: assume \(\sigma\) is known.
  3. A robust version of smash-gen: set the highest resolution wavelet coeffs to 0.
  4. Treat Poisson data as normal: only do 1 iteration.
  5. Expanding around ash posterior mean: iterate once but the Taylor series expansion is around ash posterior mean(applying ash to Poisson data first).

Binomial data with unknown nugget effect

  1. Binomial nugget effect
  2. Poisson as approximate of Binomial?

Unevenly spaced data

  1. Treat as missing data?

Credits

This project is based on the ideas from Professor Matthew Stephens. Thanks to Matthew Stephens and Kushal K Dey for their great help.


This reproducible R Markdown analysis was created with workflowr 1.0.1