Statistical inference and visualization in scale-space using local likelihood

Authors:
Cheolwoo Park;Jib Huh
Affiliations:
Department of Statistics, University of Georgia, Athens, GA 30602, USA;Department of Statistics, Duksung Women's University, Seoul 132-714, Republic of Korea
Venue:
Computational Statistics & Data Analysis
Year:
2013

Citing 7
Cited 1

Scale-Space Theory in Computer Vision

Scale-Space Theory in Computer Vision
Likelihood-based local polynomial fitting for single-index models

Journal of Multivariate Analysis
Bayesian multiscale analysis for time series data

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Feature significance in generalized additive models

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SiZer analysis for the comparison of regression curves

Computational Statistics & Data Analysis
Bayesian multiscale feature detection of log-spectral densities

Computational Statistics & Data Analysis
Detection of a change point based on local-likelihood

Journal of Multivariate Analysis

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

Artificial Intelligence in Medicine

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Abstract

SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for exploratory data analysis with statistical inference. Various SiZer tools have been developed in the last decade, but most of them are not appropriate when the response variable takes discrete values. In this paper, we develop a SiZer for finding significant features using a local likelihood approach with local polynomial estimators. This tool improves the existing one (Li and Marron, 2005) by proposing a theoretically justified quantile in a confidence interval using advanced distribution theory. In addition, we investigate the asymptotic properties of the proposed tool. We conduct a numerical study to demonstrate the sample performance of SiZer using Bernoulli and Poisson models using simulated and real examples.