[PDF] Top 20 Model Detection for Additive Models with Longitudinal Data
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Model Detection for Additive Models with Longitudinal Data
... In this subsection, we analyze data from the Multi-Center AIDS Cohort Study. The dataset contains the human immunodeficiency virus, HIV, status of 283 homosexual men who were infected with HIV during the follow-up ... See full document
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Selecting and fitting graphical chain models to longitudinal data
... using data from the birth, age 10 and age 30 ...missing data and loss from the survey, whilst keeping important information about parental background and childhood experiences of the cohort ...any ... See full document
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Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint
... Missing Data in Confirma- tory Clinical Trials from 2010 [13] explicitly considers random effects approaches ...effects models (GLMMs) in the case of a non-Gaussian response) as an approach to handling trials ... See full document
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Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring
... design for a sensor placement in a hotspot SHM system based on an additive color model and blob 90.. detection algorithm that can detect residual wave scatter from simulation data. 91.[r] ... See full document
18
Estimation of functional sparsity in nonparametric varying coefficient models for longitudinal data analysis
... ChIP-chip data contains the binding information of 106 transcription factors, among which 21 TFs are confirmed to be related to cell cycle regulation by ... See full document
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Drift Detection Based Model Selection Framework For Real-Time Anomaly Detection In Iot
... classifier model and operates on Big Data generated from numerous IoT devices to provide ...A detection mechanism that identifies the presence of concept drift was proposed by Dernsar and Bosnic ... See full document
6
Assessing non-additive effects in GBLUP model.
... genotypic data set was simulated considering the allelic frequency of ...GBLUP models with the V and M structures would show similar results due to the mathematical expression of the kinship calculation, a ... See full document
21
Reithinger, Florian (2006): Mixed models based on likelihood boosting. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... Differential Longitudinal Study which is extensively described in Lesaffre, Asefa & Verbeke (1999) is a cohort study examining the live births which took place during a one year period from September 1992 ... See full document
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Analysis of Longitudinal Data and Model Selection
... coefficient models are the natural extension of classical parametric models that provide a very important tool to explore the dynamic pattern in many scientific areas, namely health science, epidemiology, ... See full document
141
Mixed effects models for GAW18 longitudinal blood pressure data
... univariate data, so we applied them to the simulated data with only first-time measurements ...available data are more powerful than their cor- responding univariate analysis methods that only used ... See full document
5
Detection Procedure for a Single Additive Outlier and Innovational Outlier in a Bilinear Model
... Bilinear models were shown to perform well in comparison to linear model when applied to the Wőlfer sunspot data and the IBM daily common stock closing prices available in Box and Jenkins ... See full document
5
A Generative Joint, Additive, Sequential Model of Topics and Speech Acts in Patient Doctor Communication
... generative models of conversations due to the longer-term applications we have in ...this model to assess the variation in communicative approaches across dif- ferent doctors, and generative models ... See full document
11
Performance of mixed effects models in the analysis of mediated longitudinal data
... both models when errors of the measurement model followed a contami- nated normal distribution, however, the coverage prob- ability and bias remained good (power ≤ 18%, coverage probability ≥ 95% and bias ≤ ... See full document
11
Robust Inference for Time Varying Coefficient Models with Longitudinal Data
... In this paper, we consider a local M-estimation approach based on local polynomial smoother and a robusti- fied “generalized likelihood ratio (GLR)” statistic to test if parts of the coefficients are constants or of ... See full document
12
Semiparametric Mixed Models for Censored Longitudinal Data.
... We apply the proposed EM approach to the HIV viral load data from the AIDS Clinical Trials Group (ACTG) 398 study. One of the primary objectives of this study was to determine whether the dual protease inhibitor ... See full document
99
High-Resolution Association Mapping of Quantitative Trait Loci: A Population-Based Approach
... regression models are proposed for high-resolution linkage disequi- librium mapping of quantitative trait loci ...regression models, the ‘‘genotype effect model’’ and the ‘‘additive effect ... See full document
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Köhler, Meike (2017): Flexible Bayesian joint models for longitudinal biomarkers and time-to-event outcomes with applications to type 1 diabetes research. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... joint models has largely been motivated by biomedical applications such as modeling of CD4 counts and HIV progression (Wulfsohn and Tsiatis, 1997; Tsiatis and Davidian, 2001), PSA values and prostate cancer ... See full document
166
Target setting in additive models with preferences and interval data
... on additive models with interval data.An additive model can be converted to a multi-objective linear problem if information about preferences of the consumption of inputs and the production of ... See full document
7
A Bayesian Hierarchical Model For Longitudinal Data
... sufficient data and ...hierarchical models. Since such models introduce unknowns and it is needed to incorporate the uncertainty associated *Corresponding author: ... See full document
9
Causal Discovery with Continuous Additive Noise Models
... equation model with continuous addi- tive ...true data generating process can be represented by a restricted structural equation model like additive noise models, the causal graph can ... See full document
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