Open AccessJournal Article
Local Likelihood SiZer Map
Runze Li,James Stephen Marron +1 more
TL;DR: In this article, the authors proposed the local likelihood SiZer map, which is more efficient in distinguishing features than the original one, because of the inferential advantage of local likelihood approach.
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Abstract: The SiZer Map, proposed by Chaudhuri and Marron (1999), is a statistical tool for finding which features in noisy data are strong enough to be distinguished from background noise.In this paper, we propose the local likelihood SiZer map.Some simulation examples illustrate that the newly proposed SiZer map is more efficient in distinguishing features than the original one, because of the inferential advantage of the local likelihood approach.Some computational problems are addressed, with the result that the computational cost in constructing the local likelihood SiZer map is close to that of the original one.
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Citations
Sensitivity and specificity of information criteria.
TL;DR: In some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC.
Sensitivity and Specificity of Information Criteria
TL;DR: In some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC.
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CircSiZer: an exploratory tool for circular data
TL;DR: The CircSiZer as discussed by the authors presented a graphical device to assess which observed features are statistically significant, both for density and regression analysis with circular data, for analyzing the behavior of wind direction in the atlantic coast of Galicia (NW Spain) and how it has an influence over wind speed.
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Statistical Scale Space Methods
Lasse Holmström,Leena Pasanen +1 more
TL;DR: This work reviews the various statistical scale space methods proposed and mentions some of their applications.
SiZer Map for inference with additive models
TL;DR: The simplicity and flexibility of SiZer Map for the authors' purposes are highlighted from the performed empirical study with several real datasets, and the conclusions derived fromSiZer analysis with the global results derived from standard tests are compared.
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References
Generalized Linear Models
John A. Nelder,R. W. M. Wedderburn +1 more
- 01 May 1972
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.
9.7K
A reliable data-based bandwidth selection method for kernel density estimation
Simon J. Sheather,M. C. Jones +1 more
TL;DR: The key to the success of the current procedure is the reintroduction of a non- stochastic term which was previously omitted together with use of the bandwidth to reduce bias in estimation without inflating variance.
2.8K
Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method
TL;DR: In this paper, the Gauss-Newton method for calculating nonlinear least squares estimates generalizes easily to deal with maximum quasi-likelihood estimates, and a rearrangement of this produces a generalization of the method described by Nelder & Wedderburn (1972).
2.2K
SiZer for Exploration of Structures in Curves
TL;DR: Assessment of Significant ZERo crossings of derivatives results in the SiZer map, a graphical device for display of significance of features with respect to both location and scale.