Results 51 to 60 of about 6,261,355 (138)
Eigenvalue distributions of beta-Wishart matrices [PDF]
We derive explicit expressions for the distributions of the extreme eigenvalues of the Beta-Wishart random matrices in terms of the hypergeometric function of a matrix argument. These results generalize the classical results for the real (β = 1), complex
Koev, Plamen S, Edelman, Alan
core +1 more source
New estimation methods for diagnostic classification models
Abstract This paper introduces a new class of estimators for cognitive diagnosis models (CDMs) based on the Cressie–Read family of ϕ$$ \phi $$‐divergences. Focusing on the loglinear CDM (LCDM), for which joint maximum likelihood estimation (JMLE) has been shown to be consistent, we propose a joint minimum divergence estimation (JMDE) framework.
Elena Castilla
wiley +1 more source
Jacobi polynomial moments and products of random matrices
Motivated by recent results in random matrix theory we will study the distributions arising from products of complex Gaussian random matrices and truncations of Haar distributed unitary matrices.
Neuschel, Thorsten
core +1 more source
The random phase property and the Lyapunov Spectrum for disordered multi-channel systems [PDF]
A random phase property establishing in the weak coupling limit a link between quasi-one-dimensional random Schrödinger operators and full random matrix theory is advocated.
Römer, Rudolf A., Schulz-Baldes, H.
core +1 more source
Computing Skinning Weights via Convex Duality
We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
wiley +1 more source
Survey on differential estimators for 3d point clouds
Abstract Recent advancements in 3D scanning technologies, including LiDAR and photogrammetry, have enabled the precise digital replication of real‐world objects. These methods are widely used in fields such as GIS, robotics, and cultural heritage. However, the point clouds generated by such scans are often noisy and unstructured, posing challenges for ...
Léo Arnal–Anger +4 more
wiley +1 more source
Scalable Computation of Topological Abstractions for Scalar Data
Abstract Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial ...
M. Will +6 more
wiley +1 more source
Estimating High Dimensional Covariance Matrices and its Applications [PDF]
Estimating covariance matrices is an important part of portfolio selection, risk management, and asset pricing. This paper reviews the recent development in estimating high dimensional covariance matrices, where the number of variables can be greater ...
Jushan Bai, Shuzhong Shi
core
Phong‐Rodrigues Extrinsic Vector‐Field Processing
Abstract We introduce a new extrinsic discretization of tangent vector fields on triangle meshes that is continuous, with bounded derivatives that are continuous almost everywhere, supporting pointwise evaluation and integration of differential operators.
Hongyi Liu +4 more
wiley +1 more source
On Spatial Point Processes With Composition‐Valued Marks
Summary Methods for marked spatial point processes with scalar marks have seen extensive development in recent years. While the impressive progress in data collection and storage capacities has yielded an immense increase in spatial point process data with highly challenging non‐scalar marks, methods for their analysis are not equally well developed ...
Matthias Eckardt +2 more
wiley +1 more source

