Results 121 to 130 of about 24,793 (234)

Multiple Chains Markov Switching Vector Autoregression

Journal of Time Series Analysis, EarlyView.
ABSTRACT Both the U.S. stock and bond returns exhibit distinct Markovian regimes. However, because these regimes display limited coherence, conventional models typically require highly parameterized systems to adequately capture their joint distribution.
Leopoldo Catania
wiley   +1 more source

Optimal Portfolio Choice With Cross‐Impact Propagators

Mathematical Finance, EarlyView.
ABSTRACT We consider a class of optimal portfolio choice problems in continuous time where the agent's transactions create both transient cross‐impact driven by a matrix‐valued Volterra propagator, as well as temporary price impact. We formulate this problem as the maximization of a revenue‐risk functional, where the agent also exploits available ...
Eduardo Abi Jaber, Eyal Neuman, Sturmius Tuschmann   +2 more
wiley   +1 more source

Best linear unbiased estimation for varying probability with and without replacement sampling

Special Matrices, 2019
When sample survey data with complex design (stratification, clustering, unequal selection or inclusion probabilities, and weighting) are used for linear models, estimation of model parameters and their covariance matrices becomes complicated.
Haslett Stephen
doaj   +1 more source

Capital in Motion: Synthesizing the Circulation and Reproduction in a Multi‐Sector Growth Model

Metroeconomica, EarlyView.
ABSTRACT This paper analyzes Capital in Motion (CIM) in a capitalist economy, based on Karl Marx's Capital, Volume 2. It examines the circuit of capital, distinguishing between stock and flow variables, and integrates a multi‐sector growth model that combines the circuit and turnover of capital with the reproduction scheme.
Takashi Satoh
wiley   +1 more source

Panel Sequential Group Estimation of Interactive Effects Models

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT This paper proposes a novel procedure to identify latent groups in the slopes of panel data models with interactive effects. The method is straightforward to apply and relies only on closed‐form estimators when evaluating the objective function.
Ignace De Vos, Joakim Westerlund
wiley   +1 more source

Majorization relations for Hadamard products

Linear Algebra and its Applications, 1995
\textit{C. R. Johnson} and \textit{R. B. Bapat} [Linear Algebra Appl. 104, 246- 247 (1988)] have conjectured that: if \(A\) and \(B\) are \(n \times n\) positive definite matrices with Hadamard product \(A \circ B\) then, for each \(k \leq n\), the product of the \(k\) smallest of the eigenvalues of \(A \circ B\) is at least as great as the product of ...
openaire   +1 more source

scMOG: A graph neural network method for regulatory relationship‐preserving single‐cell multi‐omics integration

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Single‐cell multi‐omics sequencing technology provides a powerful tool for studying cellular heterogeneity. However, beyond the challenges of sparsity, heterogeneity, and dimensionality differences, a critical challenge in multi‐omics data integration lies in preserving the true regulatory relationships among molecular features.
Yucheng Lu, Xun Zhang, Hongwei Li
wiley   +1 more source

Some bounds for the spectral norms of some circulant matrices with generalized Jacobsthal–Lucas numbers

Mathematics Open
The purpose of this paper is to investigate the bounds of the spectral norms of some circulant matrices whose elements are a generalization of Jacobsthal–Lucas numbers called bi-periodic Jacobsthal–Lucas numbers by three different ways.
Sukran Uygun
doaj   +1 more source

Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning

NMR in Biomedicine, Volume 39, Issue 6, June 2026.
We propose a novel deep learning algorithm for predicting the myelin water fraction from multiple gradient‐echo or spin‐echo pulse sequences arising in magnetic resonance relaxometry (MRR) measurements of the human brain. Our method incorporates both regularized nonlinear least squares and pure deep learning through a concatenation paradigm known as ...
Mirage Modi, Shashank Sule, Jonathan Palumbo, Michael Rozowski, Griffin S. Hampton, Mustapha Bouhrara, Wojciech Czaja, Richard G. Spencer   +7 more
wiley   +1 more source

A Vector Representation of Multicomplex Numbers and Its Application to Radio Frequency Signals

Axioms
Hypercomplex numbers, which are multi-dimensional extensions of complex numbers, have been proven beneficial in the development of advanced signal processing algorithms, including multi-dimensional filter design, linear regression and classification.
Daniele Borio
doaj   +1 more source