Journal ArticleDOI
Regression estimation via information-weighted composite models with different dimensions
Mian Huang,Kang He,Weixin Yao +2 more
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TLDR
A new class of regression estimation methods by combining many candidate models with possibly different dimensions to address the issue of tapering effect estimation is proposed and an information-weighted composite likelihood is proposed.Abstract:
In this paper, we propose a new class of regression estimation methods by combining many candidate models with possibly different dimensions to address the issue of tapering effect estimation. An i...read more
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A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
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Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
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Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.