Estimating the Dimension of a Model
TLDR
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.Abstract:
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. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.read more
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Regression and time series model selection in small samples
TL;DR: In this article, a bias correction to the Akaike information criterion, called AICC, is derived for regression and autoregressive time series models, which is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample sample size.
Journal ArticleDOI
Survey of clustering algorithms
Rui Xu,Donald C. Wunsch +1 more
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Journal ArticleDOI
Strictly Proper Scoring Rules, Prediction, and Estimation
TL;DR: The theory of proper scoring rules on general probability spaces is reviewed and developed, and the intuitively appealing interval score is proposed as a utility function in interval estimation that addresses width as well as coverage.
Book
Bayesian networks and decision graphs
TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
ReportDOI
Efficient Tests for an Autoregressive Unit Root
TL;DR: In this paper, a modified version of the Dickey-Fuller t test is proposed to improve the power when an unknown mean or trend is present, and a Monte Carlo experiment indicates that the modified test works well in small samples.