Results 31 to 40 of about 1,244,223 (283)
Monte Carlo modified profile likelihood in models for clustered data [PDF]
, 2018 The main focus of the analysts who deal with clustered data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed effects formulation is preferred and the total number of clusters is large
Cortese, Giuliana +2 more
core +2 more sources
A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data
Austrian Journal of Statistics, 2021 Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the ...
Nasrin Lipi +2 more
doaj +1 more source
ACM Computing Surveys, 2011 In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data.
A. Algergawy +3 more
openaire +2 more sources
MIMO Multiway Relaying with Clustered Full Data Exchange: Signal Space Alignment and Degrees of Freedom [PDF]
, 2014 We investigate achievable degrees of freedom (DoF) for a multiple-input multiple-output (MIMO) multiway relay channel (mRC) with $L$ clusters and $K$ users per cluster. Each user is equipped with $M$ antennas and the relay with $N$ antennas.
Yuan, Xiaojun
core +1 more source
Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective) [PDF]
, 2016 Traditionally, actuaries have used run-off triangles to estimate reserve ("macro" models, on agregated data). But it is possible to model payments related to individual claims.
Charpentier, Arthur, Pigeon, Mathieu
core +4 more sources
The Rainbow Prim Algorithm for Selecting Putative Orthologous Protein Sequences [PDF]
, 2008 We present a selection method designed for eliminating species redundancy in clusters of putative orthologous sequences, to be applied as a post-processing procedure to pre-clustered data obtained from other methods. The algorithm can always zero-out the
Aloisio, G. +4 more
core +1 more source
Bayesian Statistical Inference for Factor Analysis Models with Clustered Data
MathematicsClustered data are a complex and frequently used type of data. Traditional factor analysis methods are effective for non-clustered data, but they do not adequately capture correlations between multiple observed individuals or variables in clustered data.
Bowen Chen, Na He, Xingping Li
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Hemoglobinopathies are prevalent globally; diagnosis is complex in high genetic admixture populations like Brazil. We report, in two pediatric siblings, the first documented cases in Brazil of heterozygosity for hemoglobin (Hb) O‐Arab with coinheritance of α‐thalassemia (αα/−α4.2; −α3.7/−α4.2), resulting in microcytic and hypochromic anemia ...
Elisângela de Souza Miranda Muynarsk +9 more
wiley +1 more source
Scalable Distributed kNN Processing on Clustered Data Streams
IEEE Access, 2019 Recommender systems provide an important tool for users to find interested items from the massive amount of user-generated contents. As user interests often change over time and contents become available in a streaming fashion, it is highly desirable to ...
Min Yang +3 more
doaj +1 more source
Systematic comparison of approaches to analyze clustered competing risks data
BMC Medical Research Methodology, 2023 Background In many clinical trials the study interest lies in the comparison of a treatment to a control group regarding a time to event endpoint like time to myocardial infarction, time to relapse, or time to a specific cause of death. Thereby, an event
Sabrina Schmitt +2 more
doaj +1 more source