Last updated: 2018-05-09

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File	Version	Author	Date	Message
Rmd	fda2411	Dongyue	2018-05-09	one iteration ash poisson
html	3a42238	Dongyue	2018-05-08	correction
Rmd	910bc07	Dongyue	2018-05-08	correction
html	cb91cb1	Dongyue	2018-05-08	add robust
Rmd	2e73919	Dongyue	2018-05-08	add robust
html	44d3413	Dongyue	2018-05-07	correct mu_t+E(u_t)
Rmd	b082c1b	Dongyue	2018-05-07	correct mu_t+E(u_t)
Rmd	ee44935	Dongyue	2018-05-06	unknown sigma version
html	767077f	Dongyue	2018-05-06	unknown sigma version
html	341e471	Dongyue	2018-05-05	edit
html	c72247b	Dongyue	2018-05-05	edit
Rmd	3bd6a61	Dongyue	2018-05-05	first commit
html	ca322e8	Dongyue	2018-05-05	first commit
Rmd	b7e89a3	DongyueXie	2018-05-01	Start workflowr project.

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.

Analysis

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

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

Smash-gen

Project Overview

Analysis

Credits