Analysis Window Length Selection For Linear Signal Models

Yazar, Alper
Candan, Çağatay
A method is presented for the selection of analysis window length, or the number of input samples, for linear signal modeling without compromising the model assumptions. It is assumed that the signal of interest lies in a known linear space and noisy samples of the signal is provided. The goal is to use as many signal samples as possible to mitigate the effect of noise without violating the assumptions on the model. An application example is provided to illustrate the suggested method.
23nd Signal Processing and Communications Applications Conference (SIU)


Application of F-test method on model order selection and related problems
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Signal modeling is one of the important topics of signal processing area. The input signal should be modeled with a suitable mathematical model first. In statistics related disciplines, there are information theory based criteria for model order selection topic. In this thesis work, F-test based methods are proposed on model order selection and related problems. F-test is used in statistics related disciplines. However, it is not so widely used in signal processing related problems. Solution approaches for ...
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Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the e...
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Citation Formats
A. Yazar and Ç. Candan, “Analysis Window Length Selection For Linear Signal Models,” presented at the 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 2015, Accessed: 00, 2020. [Online]. Available: