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Full-Text Articles in Physical Sciences and Mathematics
Initial Values In Estimation Procedures For State Space Models (Ssms), Raed Alzghool, Yan-Xia Lin
Initial Values In Estimation Procedures For State Space Models (Ssms), Raed Alzghool, Yan-Xia Lin
Faculty of Informatics - Papers (Archive)
In this paper, we will focus on State Space Models(SSMs), especially the stochastic volatility model, and lookfor a standard approach for assigning initial values in theQuasi-Likelihood (QL) and Asymptotic Quasi-Likelhood (AQL)estimation procedures.
Age Estimation Based On Extended Non-Negative Matrix Factorization, Ce Zhan, Wanqing Li, Philip Ogunbona
Age Estimation Based On Extended Non-Negative Matrix Factorization, Ce Zhan, Wanqing Li, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
Previous studies suggested that local appearance-based methods are more efficient than geometric-based and holistic methods for age estimation. This is mainly due to the fact that age information are usually encoded by the local features such as wrinkles and skin texture on the forehead or at the eye corners. However, the variations of theses features caused by other factors such as identity, expression, pose and lighting may be larger than that caused by aging. Thus, one of the key challenges of age estimation lies in constructing a feature space that could successfully recovers age information while ignoring other sources of …
Spectral Density Estimation Through A Regularized Inverse Problem, Chunfeng Huang, Tailen Hsing, Noel Cressie
Spectral Density Estimation Through A Regularized Inverse Problem, Chunfeng Huang, Tailen Hsing, Noel Cressie
Faculty of Informatics - Papers (Archive)
In the study of stationary stochastic processes on the real line, the covariance function and the spectral density function are parameters of considerable interest. They are equivalent ways of expressing the temporal dependence in the process. In this article, we consider the spectral density function and propose a new estimator that is not based on the periodogram; the estimator is derived through a regularized inverse problem. A further feature of the estimator is that the data are not required to be observed on a grid. When the regularization condition is based on the function's first derivative, we give the estimator …