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Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging
Prognostic Modeling Risk Prediction
2013/4/27
The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive applica...
Thermal Effects for Shaft-Pre-Stress on Rotor Dynamic System
Thermal effects Modelling, Campbell diagram Whirl Rotor dynamics.
2012/9/17
This Paper outlines study behaviour of rotating shaft with high speed under thermal effects. The method of obtaining the frequency response functions of a rotor system with study whirl effect in this ...
Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data
composite likelihood EM algorithm latent Markov model pairwise likelihood
2012/9/17
In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and ...
Set-valued dynamic treatment regimes for competing outcomes
Set-valued dynamic treatment regimes competing outcomes
2012/9/19
Dynamic treatment regimes operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function takes as input up-to-date patient information and...
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Re-Weighted Dynamic Filtering Time-Varying Signal Estimation
2012/9/17
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
Laplace deconvolution and its application to Dynamic Contrast Enhanced imaging
Laplace deconvolution complexity penalty Dynamic Contrast Enhanced imaging
2012/9/19
In the present paper we consider the problem of Laplace deconvolution with noisy discrete observations. The study is motivated by Dynamic Contrast Enhanced imaging using a bolus of contrast agent, a p...
Modelling outliers and structural breaks in dynamic linear models with a novel use of a heavy tailed prior for the variances: An alternative to the Inverted Gamma
Modelling outliers structural breaks Inverted Gamma
2011/7/19
In this paper we propose a new wider class of hypergeometric heavy tailed priors that are given as the convolution of a Student-t density for the location parameter and a Scaled Beta2 prior for the va...
Approximate Propagation of both Epistemic and Aleatory Uncertainty through Dynamic Systems
Uncertainty Propagation Epistemic Uncertainty Aleatory Uncertainty Dempster-Shafer
2011/7/19
When ignorance due to the lack of knowledge, modeled as epistemic uncertainty using Dempster-Shafer structures on closed intervals, is present in the model parameters, a new uncertainty propagation me...
Approximate group context tree: applications to dynamic programming and dynamic choice models
categorical time series group context tree
2011/7/19
The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities.
Revealing spatial variability structures of geostatistical functional data via Dynamic Clustering
functional data clustering geostatistics variogram
2011/7/6
In several environmental applications data are functions of time, essentially con- tinuous, observed and recorded discretely, and spatially correlated. Most of the methods for analyzing such data are ...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...
A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/24
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/24
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/23
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A dynamic hybrid model based on wavelets and fuzzy regression for time series estimation
Financial time series Wavelet decomposition Fuzzy regression SP500 index
2011/3/25
In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on ...