Empirical bias results for the data-driven Haar-Fisz transform for finite sample sizes
نویسندگان
چکیده
The data-driven Haar-Fisz (DDHF) transformation was recently developed to stabilise the variance of data with an increasing (but otherwise unknown) mean-variance relationship. This report investigates the empirical bias of the DDHF transform and compares it to the much used Box-Cox transformation, using both simulated Poisson counts and data of the deaths of coalition personnel in Iraq.
منابع مشابه
Variance stabilization with DDHFm
The DDHFm package is designed to perform data-driven Haar-Fisz (DDHF) variance stabilization. The basic DDHF method itself is described in [4, 5]. The modifications to DDHF to make it work successfully for microarray (or indeed similar kinds of replicate data) are described in [10]. The basic idea of the Haar-Fisz transform is very simple. First, a Haar wavelet transform is applied to the data....
متن کاملVariance stabilization and normalization for one-color microarray data using a data-driven multiscale approach
MOTIVATION Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean-variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the ...
متن کاملHaar-Fisz estimation of evolutionary wavelet spec- tra
We propose a new “Haar-Fisz” technique for estimating the time-varying, piecewise constant local variance of a locally stationary Gaussian time series. We apply our technique to the estimation of the spectral structure in the Locally Stationary Wavelet model. Our method combines Haar wavelets and the variance stabilizing Fisz transform. The resulting estimator is mean-square consistent, rapidly...
متن کاملTechnical Report 04:06 Smoothing the wavelet periodogram using the Haar- Fisz transform
The wavelet periodogram is hard to smooth because of the low signal-to-noise ratio and non-stationary covariance structure. This article introduces a method for smoothing a local wavelet periodogram by applying a Haar-Fisz transform which approximately Gaussianizes and approximately stabilizes the variance of the periodogram. Consequently, smoothing the transformed periodogram can take advantag...
متن کاملA Haar-Fisz Algorithm for Poisson Intensity Estimation
This article introduces a new method for the estimation of the intensity of an inhomogeneous one-dimensional Poisson process. The Haar-Fisz transformation transforms a vector of binned Poisson counts to approximate normality with variance one. Hence we can use any suitable Gaussian wavelet shrinkage method to estimate the Poisson intensity. Since the Haar-Fisz operator does not commute with the...
متن کامل