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Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models
Forward Regression Partially Linear Model Profiled Forward Regres- 9 sion Screening Consistency
2016/1/19
Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models.
QML estimation of spatial dynamic panel data models with time varying spatial weights matrices
Spatial autoregression Dynamic panels Time varying spatial weights matrix Fixed ef- fects Maximum likelihood
2016/1/19
This paper investigates the quasi-maximum likelihood estimation of spatial dynamic panel data mod-els where spatial weights matrices can be time varying. We …nd that QML estimate is consistent and asy...
Generalized additive models for medical research
Generalized additive models medical research
2015/8/21
This article reviews flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints. Applications to survival models and binary outcome...
Linear Latent Force Models using Gaussian Processes
Gaussian Processes Linear Latent Force Models
2011/7/19
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.
Statistical Topic Models for Multi-Label Document Classification
Topic Models LDA Multi-Label Classification Document Modeling
2011/7/19
Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches i...
High-Dimensional Structure Estimation in Ising Models: Tractable Graph Families
Graphical model selection Ising models Greedy algorithms
2011/7/19
We consider the problem of high-dimensional Ising (graphical) model selection. We propose a simple algorithm for structure estimation based on the thresholding of the empirical conditional mutual info...
Stochastic Search for Semiparametric Linear Regression Models
Stochastic Search Semiparametric Linear Regression Models
2011/7/6
This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar (1987).
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies
Bayesian variable selection generalized linear models Gaussian processes
2011/7/5
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data.
A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity.
Spatial wavelet Markov models are more efficient than covariance tapering and process convolutions
Matérn covariances Kriging Wavelets Markov random fields Covariance tapering
2011/7/5
The Mat\'ern covariance function is a popular choice for modeling dependence in spatial environmental data.
Estimating and Understanding Exponential Random Graph Models
Random graph Erd-os-Renyi graph limit Exponential Random Graphs param-eter estimation
2011/3/25
We introduce a new method for estimating the parameters of exponential random graph models. The method is based on a large-deviations approximation to the normalizing constant shown to be consistent u...
Uniqueness of multi-dimensional infinite volume self-organized critical forest-fire models
multi-dimensional infinite forest-fire models
2009/4/3
In a forest-fire model, each site of the square lattice is either vacant or occupied by a tree. Vacant sites get occupied according to independent rate 1 Poisson processes. Independently at each site ...
Multidimensional latent Markov models in a developmental study of inhibitory control and attentional flexibility in early childhood
dimensionality assessment executive function item response theory latentMarkov model Rasch model two-parameter logistic parameterisation
2010/3/17
We demonstrate the use of a multidimensional extension of the latent Markov model
to analyse data from studies with correlated binary responses in developmental psychology.
In particular, we conside...