Results 211 to 220 of about 1,229,920 (265)

Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach. [PDF]

BMC Bioinformatics
Loeloe MS, Tabatabaei SM, Sefidkar R, Mehrparvar AH, Jambarsang S.   +4 more
europepmc   +1 more source

Unveiling overeating patterns within digital longitudinal data on eating behaviors and contexts. [PDF]

NPJ Digit Med
Shahabi F, Wei B, Romano C, McCloskey R, Lin AW, Pedram M, Schauer JM, Stump T, Alshurafa N.   +8 more
europepmc   +1 more source

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Modeling Nonstationary Longitudinal Data

Biometrics, 2000
Summary. An important theme of longitudinal data analysis in the past two decades has been the development and use of explicit parametric models for the data's variance‐covariance structure. A variety of these models have been proposed, of which most are second‐order stationary.
Núñez-Antón, Vicente, Zimmerman, Dale L.   +1 more
openaire   +2 more sources

A longitudinal analysis of data breaches

Information Management & Computer Security, 2011
PurposeThe purpose of this research is to provide companies and consumers with information about the potential connections between data breach types and institutions. This study also aims to add to the body of knowledge about data breaches.Design/methodology/approachThis study analyzes a chronology of five years of data breaches.
Chlotia Posey Garrison, Matoteng Ncube
openaire   +1 more source

Median regression for longitudinal data

Statistics in Medicine, 2003
AbstractWe review and compare three estimators of median regression in linear models with longitudinal data. The estimators are constructed based on well‐known ideas of weighting, decorrelating, and the working assumption of independence. Both asymptotic efficiency calculations and finite‐sample Monte Carlo studies are used to assess the performance of
He, X, Fung, WK, Fu, B
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A Note on Longitudinal Data Presentation

Research on Aging, 1987
An example of presenting longitudinal data is provided using a subsample drawn from the Retirement History Study. A progression of age groups through the years of follow-up is examined in tabular form with respect to the number of parents and parents-in-law reported living. Comparisons are made between information that may be inferred from longitudinal
L R, Hatch, E F, Borgatta
openaire   +2 more sources

Longitudinal data model selection

Computational Statistics & Data Analysis, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rahman Azari, Lexin Li, Chih-Ling Tsai
openaire   +1 more source

POLYNOMIALS WITH ASYMPTOTES FOR LONGITUDINAL DATA

Statistics in Medicine, 1996
I use Laguerre polynomials to model growth curves or time--response curves known to approach an asymptote as time approaches infinity. An example is with measurements on a variable or variables from subjects recovering from surgery. These variables can often vary in a non-monotonic fashion for which a functional form of the curve is unknown.
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Kernel Embeddings of Longitudinal Data

2016
Longitudinal data is the repeated observations of individuals through time. They often exhibit rich statistical qualities, such as skew or multimodality, that are difficult to capture using traditional parametric methods. To tackle this, we build a non-parametric Markov transition model for longitudinal data. Our approach uses kernel mean embeddings to
Darren Shen, Fabio Ramos 0001
openaire   +1 more source