Array and Statistical Signal Processing by Abdelhak M. Zoubir, Mats Viberg, Rama Chellappa and Sergios

By Abdelhak M. Zoubir, Mats Viberg, Rama Chellappa and Sergios Theodoridis (Eds.)

This 3rd quantity of a 5 quantity set, edited and authored by way of global top specialists, offers a evaluation of the rules, equipment and methods of significant and rising learn subject matters and applied sciences in array and statistical sign processing.

With this reference resource you will:

  • Quickly take hold of a brand new zone of research 
  • Understand the underlying rules of a subject matter and its application
  • Ascertain how a subject pertains to different components and examine of the examine matters but to be resolved

    • Quick instructional stories of vital and rising issues of study in array and statistical sign processing
    • Presents center rules and indicates their application
    • Reference content material on middle ideas, applied sciences, algorithms and purposes
    • Comprehensive references to magazine articles and different literature on which to construct additional, extra particular and certain wisdom
    • Edited by means of leading people in the sector who, via their popularity, were capable of fee specialists to write down on a specific topic

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    Tibshirani, Least angle regression, Ann. Stat. 32 (2) (2004) 407–451. [25] S. Arlot, A. Celisse, A survey of cross-validation procedures for model selection, Stat. Surv. 4 (2010) 40–79. [26] B. Efron, Bootstrap methods: another look at the jackknife, Ann. Stat. 7 (1) (1979) 1–26. [27] R. M. Zoubir, P. Pelin, Detection of sources using bootstrap techniques, IEEE Trans. Signal Process. 50 (2) 206–215. [28] R. Nishii, Asymptotic properties of criteria for selection of variables in multiple regression, Ann.

    13) as GIC = −2 (θˆ |y) + C N · 2K can often outperform AIC for C N > 1. For example, when C N = C N the AICc is obtained and the choice C N = (1/2) log N yields the MDL criterion. 2 Minimum Description Length The Minimum Description Length [11] method stems from the algorithmic information and coding theory. The basic idea is to find any regularity in the observed data that can be used to compress the data. This allows for condensing the data so that less symbols are needed to present it. The code used for compressing the data should be such that the total length (in bits) of description of the code and the description of the data is minimal.

    If cross-validation is not used, adding more predictor variables in the model decreases the risk or error measure, such as mean square error, sum of squared residuals or classification error for the training set but the predictive performance may be very different. A few remarks on the statistical performance of the CV method are in place. CV gives a too positive view on the quality of the model optimized for the training set. The risk or error measure such as MSE will be smaller for the training set than the one for the test set.

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