The Monte-Carlo Sampling Approach to Model Selection: A Primer [Lecture Notes]

@article{Stoica2022TheMS,
  title={The Monte-Carlo Sampling Approach to Model Selection: A Primer [Lecture Notes]},
  author={Petre Stoica and Xiaolei Shang and Yuanbo Cheng},
  journal={IEEE Signal Processing Magazine},
  year={2022},
  volume={39},
  pages={85-92}
}
Any data modeling exercise has two main components: parameter estimation and model selection. The latter will be the topic of this lecture note. More concretely, we introduce several Monte-Carlo sampling-based rules for model selection using the maximum a posteriori (MAP) approach. Model selection problems are omnipresent in signal processing applications: examples include selecting the order of an autoregressive predictor, the length of the impulse response of a communication channel, the… 
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References

SHOWING 1-10 OF 11 REFERENCES

Model-order selection: a review of information criterion rules

The parametric (or model-based) methods of signal processing often require not only the estimation of a vector of real-valued parameters but also the selection of one or several integer-valued…

Marginal likelihood computation for model selection and hypothesis testing: an extensive review

This article provides a comprehensive study of the state-of-the-art of marginal likelihood computation for model selection and hypothesis testing, highlighting limitations, benefits, connections and differences among the different techniques.

On the Proper Forms of BIC for Model Order Selection

The conclusion of this study is that BIC remains one of the most successful existing rules for model order selection, if properly used.

Monte Carlo Statistical Methods

In comparing nonparametric and parametric methods of time series analysis, the book summarizes the results of Carbon and Delecroix (1993), who considered several simulated autoregressive moving average (ARMA) processes.

Monte Carlo Strategies in Scientific Computing

The strength of this book is in bringing together advanced Monte Carlo methods developed in many disciplines, including the Ising model, molecular structure simulation, bioinformatics, target tracking, hypothesis testing for astronomical observations, Bayesian inference of multilevel models, missing-data problems.

Fundamentals of statistical signal processing: estimation theory

Minimum variance unbiased estimation Cramer-Rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares…

Cross-validation rules for order estimation

Estimation, inference, and specification analysis

The underlying motivation for maximum-likelihood estimation is explored, the interpretation of the MLE for misspecified probability models is treated, and the conditions under which parameters of interest can be consistently estimated despite misspecification are given.

System Identification I

Monte Carlo theory, methods and examples

  • 2013