Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Making linear prediction perform like maximum likelihood in Gaussian autoregressive model parameter estimation
Date
2020-01-01
Author
Candan, Çağatay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
256
views
0
downloads
Cite This
A two-stage method for the parameter estimation of Gaussian autoregressive models is proposed. The proposed first stage is an improved version of the conventional forward-backward prediction method and can be interpreted as its weighted version with the weights derived from the arithmetic mean of the log-likelihood functions for different conditioning cases. The weighted version is observed to perform better than the conventional forward-backward prediction method and other linear prediction based methods (correlation method, covariance method, Burg's method etc.) in terms of attained likelihood value. The proposed second stage uses the estimate of the first stage as the initial condition and approximates the highly non-linear log-likelihood function with a quadratic function around the initial estimate. The optimization of the quadratic cost function yields the optimal perturbation vector that locally maximizes the likelihood in the vicinity of the initial condition. The proposed method is compared with other methods and it has been observed that the likelihood value attained at the end of two-stages is almost identical to the value attained by higher complexity numerical-search based optimization tools in a wide range of experiments. The maximum likelihood-like performance at a significantly lower implementation cost makes the proposed method especially valuable for the applications with short data-records and limited computational resources.
Subject Keywords
Control and Systems Engineering
,
Signal Processing
,
Electrical and Electronic Engineering
,
Software
,
Computer Vision and Pattern Recognition
URI
https://hdl.handle.net/11511/38021
Journal
SIGNAL PROCESSING
DOI
https://doi.org/10.1016/j.sigpro.2019.107256
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
A unified framework for derivation and implementation of Savitzky-Golay filters
Candan, Çağatay (Elsevier BV, 2014-11-01)
The Savitzky-Golay (SG) filter design problem is posed as the minimum norm solution of an underdetermined equation system. A unified SG filter design framework encompassing several important applications such as smoothing, differentiation, integration and fractional delay is developed. In addition to the generality and flexibility of the framework, an efficient SG filter implementation structure, naturally emerging from the framework, is proposed. The structure is shown to reduce the number of multipliers i...
Low-level multiscale image segmentation and a benchmark for its evaluation
Akbaş, Emre (Elsevier BV, 2020-10-01)
In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that d...
Deconvolution and preequalization with best delay LS inverse filters
Tuncer, Temel Engin (Elsevier BV, 2004-11-01)
A new method for finding the best delay for the design of least-squares (1,S) inverse filters is introduced. It is shown that there is a considerable difference between the LS errors of a best delay filter and an arbitrary LS inverse filter. Proposed method is an effective and computationally efficient approach for the design of LS optimum filters. Deconvolution problem is considered and the MSE performances of pseudoinverse, preequalization and LS inverse filtering are investigated. In this respect, the th...
Properties of the momentum LMS algorithm
Tugay, Mehmet Ali; Tanik, Yalçin (Elsevier BV, 1989-10)
One of the most recent modifications on Widrow and Hoff's LMS algorithm has been the inclusion of a momentum term into the weight update equation. The resulting algorithm is referred to as “The Momentum LMS (MLMS) algorithm”. This paper revises the basic properties of the MLMS algorithm for stationary inputs. As a result, new bounds, on the parameters of the algorithm, for convergence are found, and it is shown that, under slow convergence conditions, this new algorithm is equivalent to the usual LMS algori...
Multiple description coding of animated meshes
Bici, M. Oguz; Akar, Gözde (Elsevier BV, 2010-11-01)
In this paper, we propose three novel multiple description coding (MDC) methods for reliable transmission of compressed animated meshes represented by series of 3D static meshes with same connectivity. The proposed methods trade off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The methods are based on layer duplication and partitioning of the set of vertices of a scalable coded animated mesh by either spatial or temporal sub...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Ç. Candan, “Making linear prediction perform like maximum likelihood in Gaussian autoregressive model parameter estimation,”
SIGNAL PROCESSING
, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38021.