Results 201 to 210 of about 109,032 (273)

An Uncertainty Based Approach for Dealing With Selection Bias in Non‐Probability Samples

International Statistical Review, EarlyView.
Summary The main issue with non‐probability samples is that the standard design‐based approach cannot be applied as the selection mechanism is unknown. In this paper, the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non‐probability sample, is discussed.
Pier Luigi Conti, Daniela Marella
wiley   +1 more source

FedGDAN: Privacy-preserving traffic flow prediction via federated graph diffusion attention networks. [PDF]

Sci Rep
Li Y, Mi B, Zeng R.
europepmc   +1 more source

On Metric Choice in Dimension Reduction for Fréchet Regression

International Statistical Review, EarlyView.
Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale, Congli Ma, Siyu Chen, Obed Koomson   +3 more
wiley   +1 more source

Lightweight Federated Learning Approach for Resource-Constrained Internet of Things. [PDF]

Sensors (Basel)
Baqer M.
europepmc   +1 more source

A Comparative Review of Specification Tests for Diffusion Models

International Statistical Review, EarlyView.
Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez, D. Diz‐Castro, M. Febrero‐Bande, W. González‐Manteiga   +3 more
wiley   +1 more source

Unconditional Variance Estimation Under Complex Surveys

International Statistical Review, EarlyView.
Summary The unconditional framework treats the samples and the variables of interest as random variables. This is particularly suitable with analytic inference, when modelling survey data. We show that variance estimation does not involve finite population corrections and joint‐inclusion probabilities, even with large sampling fractions and under ...
Yves G. Berger
wiley   +1 more source

Relativistic triangle-curvature computing for federated HIV-1 protein-sequence monitoring. [PDF]

Sci Rep
Villalba-Díez J, González-Marcos A.
europepmc   +1 more source

Robust Distance Covariance

International Statistical Review, EarlyView.
Summary Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual distance covariance is bounded, but that its breakdown value is zero.
Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw   +2 more
wiley   +1 more source

FedECPA: An Efficient Countermeasure Against Scaling-Based Model Poisoning Attacks in Blockchain-Based Federated Learning. [PDF]

Sensors (Basel)
Olapojoye R, Salman T, Baza M, Alshehri A.   +3 more
europepmc   +1 more source

Inference via the Skewness‐Kurtosis Set

International Statistical Review, EarlyView.
Summary Kurtosis minus squared skewness is bounded from below by 1, but for unimodal distributions, this parameter is bounded by 189/125. In some applications, it is natural to compare distributions by comparing their kurtosis‐minus‐squared‐skewness parameters. The asymptotic behavior of the empirical version of this parameter is studied here for i.i.d.
Chris A. J. Klaassen, Bert van Es
wiley   +1 more source