Results 231 to 240 of about 138,910 (273)

Joint Estimation and Bandwidth Selection in Partially Parametric Models

Journal of Applied Econometrics, Volume 41, Issue 3, Page 227-239, April/May 2026.
ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown ...
Daniel J. Henderson, Nadine McCloud, Christopher F. Parmeter   +2 more
wiley   +1 more source

Imperfect Synthetic Controls

Journal of Applied Econometrics, Volume 41, Issue 3, Page 253-264, April/May 2026.
ABSTRACT The synthetic control method assumes the existence of a perfect synthetic control, which cannot exist if the outcomes are functions of transitory shocks with nonzero asymptotic variance and may not exist even in expectation for the treated unit. This paper first shows the benefits of estimating synthetic controls for all units.
David Powell
wiley   +1 more source

Count Data Models With Heterogeneous Peer Effects Under Rational Expectations

Journal of Applied Econometrics, Volume 41, Issue 3, Page 295-309, April/May 2026.
ABSTRACT This paper develops a peer effect model for count responses under rational expectations. The model accounts for heterogeneity in peer effects across groups based on observed characteristics. Identification is based on the linear model condition that requires the presence of friends of friends who are not direct friends.
Aristide Houndetoungan
wiley   +1 more source

Scheduling Bodyguards

Naval Research Logistics (NRL), Volume 73, Issue 3, Page 334-351, April 2026.
ABSTRACT Security agencies around the world use bodyguards to protect government officials and public figures. In this paper, we consider a two‐person zero‐sum game between a defender who allocates such bodyguards to protect several targets and an attacker who chooses one target to attack.
Loe Schlicher, Kyle Y. Lin, Moshe Kress
wiley   +1 more source

Approximation of the Pseudospectral Abscissa via Eigenvalue Perturbation Theory

Numerical Linear Algebra with Applications, Volume 33, Issue 2, April 2026.
ABSTRACT Reliable and efficient computation of the pseudospectral abscissa in the large‐scale setting is still not settled. Unlike the small‐scale setting where there are globally convergent criss‐cross algorithms, all algorithms in the large‐scale setting proposed to date are at best locally convergent.
Waqar Ahmed, Emre Mengi
wiley   +1 more source

Loss Behavior in Supervised Learning With Entangled States

Advanced Quantum Technologies, Volume 9, Issue 4, April 2026.
Entanglement in training samples supports quantum supervised learning algorithm in obtaining solutions of low generalization error. Using analytical as well as numerical methods, this work shows that the positive effect of entanglement on model after training has negative consequences for the trainability of the model itself, while showing the ...
Alexander Mandl, Johanna Barzen, Marvin Bechtold, Frank Leymann, Lavinia Stiliadou   +4 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Nonsmooth nonnegative matrix factorization (nsNMF)

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an ...
Alberto, Pascual-Montano, J M, Carazo, Kieko, Kochi, Dietrich, Lehmann, Roberto D, Pascual-Marqui   +4 more
openaire   +4 more sources

Nonnegative Discriminant Matrix Factorization

IEEE Transactions on Circuits and Systems for Video Technology, 2017
Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification.
Yuwu Lu, Zhihui Lai, Yong Xu, Xuelong Li, David Zhang, Chun Yuan   +5 more
openaire   +4 more sources

Unilateral Orthogonal Nonnegative Matrix Factorization

SIAM Journal on Control and Optimization, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shang, Jun, Chen, Tongwen
openaire   +2 more sources