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Group-Sparse Model Selection: Hardness and Relaxations
Signal Approximation Structured Sparsity Interpretability Tractability Dynamic Programming Compressive Sensing
2013/5/2
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these model...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
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...
Fast dimension-reduced climate model calibration
Fast dimension-reduced climate model calibration
2013/4/27
What is the response of the climate system to anthropogenic forcings? This question is addressed typically using projections from climate models. The uncertainty surrounding current climate projection...
The Future Has Thicker Tails than the Past: Model Error As Branching Counterfactuals
Fukushima Counterfactual histories Risk management Epistemology of probability Model errors Fragility and Antifragility Fourth Quadrant
2012/11/23
Ex ante forecast outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. Reapplying measurements of uncertainty about the estimation errors...
Model selection and estimation of a component in additive regression
Model selection estimation component additive regression
2012/11/23
Let $Y\in\R^n$ be a random vector with mean $s$ and covariance matrix $\sigma^2P_n\tra{P_n}$ where $P_n$ is some known $n\times n$-matrix. We construct a statistical procedure to estimate $s$ as well ...
Criteria for Bayesian model choice with application to variable selection
Model selection variable selection objective Bayes.
2012/11/23
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propos...
Criteria for Bayesian model choice with application to variable selection
Model selection variable selection objective Bayes.
2012/11/23
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propos...
Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values
Nuisance Parameters Post-Model-Selection Random Critical Values
2012/11/22
We point out that the ideas underlying some test procedures recently proposed for testing post-model-selection (and for some other test problems) in the econometrics literature have been around for qu...
On the impossibility of constructing good population mean estimators in a realistic Respondent Driven Sampling model
impossibility constructing good population mean estimators realistic Respondent Driven Sampling model
2012/11/22
Current methods for population mean estimation from data collected by Respondent Driven Sampling (RDS) are based on the Horvitz-Thompson estimator together with a set of assumptions on the sampling mo...
Spatial two tissue compartment model for DCE-MRI
Gaussian Markov randomelds hierarchical Bayesia nmodel multi-compartment models nonlinear regression oncology spatial regularisation
2012/11/22
In the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) compartment models allow to describe the uptake of contrast medium with biological meaningful kinetic par...
Evolutionary Model of the Growth and Size of Firms
firm size distribution firm growth Gibrat's law product growth size-variance relationship growth rate distribution, Subbotin distribution Laplace distribution Pareto distribution price distribution human activity evolutionary economics product life cycle learning curve market size Hendersons law law of diminishing returns competitive markets
2012/9/18
The key idea of this model is that firms are the result of an evolutionary process. Based on demand and supply considerations the evolutionary model presented here derives explic...
Bayesian Analysis of Multiway Tables in Association Studies: A Model Comparison Approach
Bayesian Analysis Multiway Tables Association Studies Model Comparison Approach
2012/9/17
We consider the problem of statistical inference on unknown quantities structured as a multiway table. We show that such multiway tables are naturally formed by arranging regression coecients in comp...
A non-parametric mixture model for topic modeling over time
non-parametric mixture model topic modeling over time
2012/9/17
A single, stationary topic model such as la-tent Dirichlet allocation is inappropriate for modeling corpora that span long time peri-ods, as the popularity of topics is likely to change over time. A n...
Simultaneous Model Selection and Estimation for Mean and Association Structures with Clustered Binary Data
association clustered binary data generalized estimating equation logistic regression variable selection
2012/9/17
This paper investigates the property of the penalized estimating equations when both the mean and association structures are modelled. To select variables for the mean and association structures seque...