Mixture structure analysis using the Akaike Information Criterion and the bootstrap
نویسندگان
چکیده
JEFFREY L. SOLKA*, EDWARD J. WEGMAN, CAREY E. PRIEBE, WENDY L. POSTON and GEORGE W. ROGERS Dahlgren Division of the Naval Surface Warfare Center, Systems Research and Technology Department, Advanced Computation Technology Group, Code B10, Dahlgren VA 22448-5100, USA Center for Computational Statistics, George Mason University, Fairfax, VA 22030-4444, USA Department of Mathematical Sciences, The Johns Hopkins University, Baltimore, MD 21218, USA
منابع مشابه
Bootstrap variants of the Akaike information criterion for mixed model selection
Two bootstrap-corrected variants of the Akaike information criterion are proposed for the purpose of small-sample mixed model selection. These two variants are asymptotically equivalent, and provide asymptotically unbiased estimators of the expected Kullback-Leibler discrepancy between the true model and a fitted candidate model. The performance of the criteria is investigated in a simulation s...
متن کاملUsing the bootstrap to select models
In this paper the problem of model selection is addressed by the Bayesian methodology and the bootstrap technique. As a rule for choosing the best model from a set of proposed models, the maximum a posteriori principle is used. The evaluation of the maximuma posteriori probability (MAP) of each model amounts to computation of integrals whose integrands may be very peaked functions. We carry out...
متن کاملVariable Selection in Finite Mixture of Regression Models
In the applications of finite mixture of regression (FMR) models, often many covariates are used, and their contributions to the response variable vary from one component to another of the mixture model. This creates a complex variable selection problem. Existing methods, such as the Akaike information criterion and the Bayes information criterion, are computationally expensive as the number of...
متن کاملDetecting fish in underwater video using the EM algorithm
We consider the problem of detecting fish in underwater video. We adopt a modeling framework, where the shape of each fish is assumed to be multivariate Gaussian. Mixture modeling is used to classify noise and varying numbers of fish. The mixture parameters are estimated using an EM algorithm that incorporates an Akaike information criterion to simultaneously estimate the number of components i...
متن کاملExtending the Akaike Information Criterion to Mixture Regression Models
We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find that the Akaike information criterion is unsatisfactory for this purpose because it overestimates the number of components, which in turn results in incorrect variables being retained in the model. Therefore, we derive a new information criterion, the mixture regressio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Statistics and Computing
دوره 8 شماره
صفحات -
تاریخ انتشار 1998