Results 81 to 90 of about 108,134 (338)

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence

Entropy, 2018
This paper presents a covariance matrix estimation method based on information geometry in a heterogeneous clutter. In particular, the problem of covariance estimation is reformulated as the computation of geometric median for covariance matrices ...
Xiaoqiang Hua, Yongqiang Cheng, Hongqiang Wang, Yuliang Qin   +3 more
doaj   +1 more source

Polarization measurements analysis II. Best estimators of polarization fraction and angle

, 2014
With the forthcoming release of high precision polarization measurements, such as from the Planck satellite, it becomes critical to evaluate the performance of estimators for the polarization fraction and angle.
Alina, D., Bernard, J. -P., Guillet, V., Levrier, F., Montier, L., Plaszczynski, S., Ristorcelli, I., Tristram, M.   +7 more
core   +2 more sources

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation [PDF]

Econometrica, 1991
This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme.
openaire   +1 more source

Size Matters: Covariance Matrix Estimation Under the Alternative [PDF]

SSRN Electronic Journal, 2005
Summary: The purpose of this paper is to investigate, using Monte Carlo methods, whether \textit{A. R. Hall}'s [Econometrica 68, No. 6, 1517--1527 (2000; Zbl 1015.62123)] centred test of overidentifying restrictions for parameters estimated by the generalized method of moments (GMM) is more powerful, once the test is size-adjusted, than the standard ...
openaire   +4 more sources

Factors Driving Battery and Solar Purchase Decision of Residents: a Behavioural Choice Experiment Using a Hybrid Discrete Choice and Latent Variable Model

Advanced Sustainable Systems, EarlyView.
This article explores what drives households to adopt solar PV and battery systems in South East Queensland. Using hybrid discrete choice experiments, it reveals distinct adopter profiles and highlights cost, system size, and energy independence as key motivators.
Mohammad Alipour, Elnaz Irannezhad, Rodney A. Stewart, Oz Sahin   +3 more
wiley   +1 more source

Object-oriented Computation of Sandwich Estimators

Journal of Statistical Software, 2006
Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification.
Achim Zeileis
doaj   +3 more sources

Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market [PDF]

The objective of this paper is to examine effects of realized covariance matrix estimators based on intraday returns on large-scale minimum-variance equity portfolio optimization.
Masato Ubukata
core  

Inertia Estimation Through Covariance Matrix

IEEE Transactions on Power Systems
This work presents a technique to estimate on-line the inertia of a power system based on ambient measurements. The proposed technique utilizes the covariance matrix of these measurements and solves an optimization problem that fits such measurements to the synchronous machine classical model.
Federico Bizzarri, Davide del Giudice, Samuele Grillo, Daniele Linaro, Angelo Brambilla, Federico Milano   +5 more
openaire   +2 more sources

Machine‐Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles

Advanced Science, EarlyView.
Using machine learning on a mega‐scale global dataset (n = 1,336,840) reveals a robust personality trait architecture beyond the Big Five. A Big Two model, broadly capturing social engagement and internal mentation, defines a geometric space that links personality to neurocognitive profiles.
Kaixiang Zhuang, Ji Chen, Jinfeng Han, Wei Cheng, Jiang Qiu, Jianfeng Feng, Simon Eickhoff, Deniz Vatansever   +7 more
wiley   +1 more source