Longitudinal data
Model Detection for Additive Models with Longitudinal Data
... with longitudinal data has also been considered by some ...with longitudinal data have not been investi- ...with longitudinal data; 2) We develop theoretical properties for our ...
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Modelling and estimation for the genetic analysis of longitudinal data
... We assume the observed pheotypic tragectory can be decomposed as Y(t)=µ(t) + g(t) + ,e(t) +ε ,where µ(t) is a function of t,g(t) is the genotypic mean function of Y(t), ε is the residual variation ,assumed normally ...
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Law, Finance and Development: Further Analyses of Longitudinal Data
... The longitudinal data on 60 variables indicating in each case some aspect of shareholder protection have been assembled by the legal members of the CBR team for the period ...such data could only be ...
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Sample size determination for mediation analysis of longitudinal data
... using longitudinal data allows re- searchers to investigate biological pathways and identi- fies their direct and indirect contribution to interested outcome ...for longitudinal data to ...
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Handling attrition and non-response in longitudinal data
... transforming data to an underlying multivariate normal ...available data it can be expected to provide maximum efficiency, and we have illustrated the efficiency gain with a simple ...In longitudinal ...
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Imputation Methods for Longitudinal Data: A Comparative Study
... of longitudinal data with missing ...missing data and concluded that these methods are only appropriate under the strong assumption of MCAR ...
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Advanced Statistical Methods for Complex Longitudinal Data.
... represent longitudinal or repeated measures ...censored longitudinal data in which the random effects are represented by the flexible seminonparametric (SNP) density and show how to obtain estimates ...
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Efficiency of Selecting Important Variable for Longitudinal Data
... in longitudinal data with application to ...this longitudinal educational data (Test of English for International Communication, TOEIC), this study compares multiple regression, backward ...
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Semiparametric Mixed Models for Censored Longitudinal Data.
... distinct data points as knots and meanwhile introduces a penalty for controlling the smoothness of the estimates, and hence it is considered to be a more data-adaptive smoothing ...
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The analysis of multivariate longitudinal data: An instructive application of the longitudinal three-mode model.
... large longitudinal data ...of longitudinal measurement invariance; (2) residual factor covariances and correla- tions, and additional restrictions to test equality of variances, correlations and ...
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A semiparametric regression model for longitudinal data with non stationary errors
... National Longitudinal Surveys (NLS) are a set of surveys designed for gathering information at multiple points in time on the labor market activities and other significant events of several groups of men and ...
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Analysis of Longitudinal Data and Model Selection
... of longitudinal data is model parsimony, that is, finding a model with as few regression variables as possible while retaining good properties of the parameter ...
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A Bayesian Hierarchical Model For Longitudinal Data
... of longitudinal data from a randomized controlled clinical tuberculosis ...trial. Data for each subject are observed on thirteen time point of occasions of the ...the data set is that ...
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Transitional modeling of experimental longitudinal data with missing values
... methodology, data from the McKinney Homeless Research Project (MHRP) in San Diego as described in chapters 10 and 11 in the book by Hedeker and Gibbons (2006), will be ...this longitudinal study and were ...
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New GOLD classification: longitudinal data on group assignment
... no data has been published about evaluation with tools such as the CAT or CCQ ...annual longitudinal progression of disease evaluated by the new GOLD proposal has not yet been ...
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Data Preparation for Longitudinal Data Mining: a case study on human ageing
... the longitudinal version of the ELSA database, only 275 have been kept after this task, considered the most relevant to the ...the data preparation process, and before using mining algorithms, providing a ...
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First CLADAG data mining prize : data mining for longitudinal data with different marketing campaigns
... The organisers of the competition did not specify whether the time periods have constant length, whether they overlap and whether they are strictly sequential, that is, there are no gaps between them. Note that the ...
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Selecting and fitting graphical chain models to longitudinal data
... A chain graph is a well recognised tool to specify causal relationships amongst processes (Pearl, 1995). The variables are ordered a priori, as shown, for example, in Figure 3b. The model is specified according to theory ...
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Towards longitudinal data analytics in Parkinson's Disease
... The performance of the CloudUPDRS signal processing toolkit was validated on a bespoke data set comprised of 20 complete sets of measurements. Results obtained from its application were compared against the same ...
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Multivariate spatial statistical analysis of longitudinal data in perennial crops
... standardised data (divided by the phenotypic standard deviation from individual analysis for each measure), the associated repeatability coefficient was ...the data in original scale) by the repeatability ...
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