Functional lagged regression with sparse noisy observations
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Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...
Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...
During the warm summer of 2006 a comprehensive longitudinal field survey of the adaptive actions of occupants, their thermal satisfaction and the coincident environmental conditions was conducted in eight Swiss offices. Based on analysis of these results w ...
Autoregressive modeling is applied for approximating the temporal evolution of spectral density in critical-band-sized sub-bands of a segment of speech signal. The generalized autocorrelation linear predictive technique allows for a compromise between fitt ...
This letter provides a unified mean-square performance analysis of the class of data reusing adaptive algorithms. The derivation relies on energy conservation arguments, and it does not restrict the regression data to being Gaussian. Simulation results sho ...
Multimodal biometric authentication (BA) has shown perennial successes both in research and applications. This paper casts a light on why BA systems can be improved by fusing opinions of different experts, principally due to diversity of biometric modaliti ...
We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...
The results of a previous paper claiming the demonstration that edge localized mode (ELM) dynamics on TCV are chaotic in a number of cases has recently been called into question, because the statistical test employed was found to also identify linear auto ...
Autoregressive modeling is applied for approximating the temporal evolution of spectral density in critical-band-sized sub-bands of a segment of speech signal. The generalized autocorrelation linear predictive technique allows for a compromise between fitt ...
Frequency Domain Linear Prediction (FDLP) represents the technique for approximating temporal envelopes of a signal using autoregressive models. In this paper, we propose a wide-band audio coding system exploiting FDLP. Specifically, FDLP is applied on cri ...
