Results 241 to 250 of about 2,661 (306)
Restricted Tweedie stochastic block models
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
A Post-Quantum Public-Key Signcryption Scheme over Scalar Integers Based on a Modified LWE Structure. [PDF]
Kara M, Hammoudeh M, Alamri A, Alamri S.
europepmc +1 more source
A Simple Improvement for Integer Factorizations with Implicit Hints
Ryuichi Harasawa +2 more
openalex +2 more sources
An observation‐driven state‐space model for claims size modelling
Abstract State‐space models are popular in econometrics. Recently, these models have gained some popularity in the actuarial literature. The best known state‐space models are of the Kalman‐filter type. These are called parameter‐driven because the observations do not impact the state‐space dynamics.
Jae Youn Ahn +2 more
wiley +1 more source
Optimized quantum folding Barrett reduction for quantum modular multipliers. [PDF]
Zhang J, Cho SM, Lee C, Seo SH.
europepmc +1 more source
Integer Polynomial Factorization by Recombination of Real Factors: Re-evaluating an Old Technique in Modern Era [PDF]
Shahriar Iravanian
openalex +1 more source
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne +2 more
wiley +1 more source
Latent Class Analysis with Arbitrary-Distribution Responses. [PDF]
Qing H, Xu X.
europepmc +1 more source
Asymptotic properties of cross‐classified sampling designs
Abstract We investigate the family of cross‐classified sampling designs across an arbitrary number of dimensions. We introduce a variance decomposition that enables the derivation of general asymptotic properties for these designs and the development of straightforward and asymptotically unbiased variance estimators.
Jean Rubin, Guillaume Chauvet
wiley +1 more source

