Sparse high-dimensional models in economics
This article reviews the literature on sparse high-dimensional models and discusses some
applications in economics and finance. Recent developments in theory, methods, and …
applications in economics and finance. Recent developments in theory, methods, and …
[图书][B] Bayesian data analysis
Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …
analyses from a Bayesian perspective. Using examples largely from the authors' own …
Sparse permutation invariant covariance estimation
The paper proposes a method for constructing a sparse estimator for the inverse covariance
(concentration) matrix in high-dimensional settings. The estimator uses a penalized normal …
(concentration) matrix in high-dimensional settings. The estimator uses a penalized normal …
[图书][B] Time series: modeling, computation, and inference
R Prado, M West - 2010 - taylorfrancis.com
Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …
High dimensional covariance matrix estimation using a factor model
High dimensionality comparable to sample size is common in many statistical problems. We
examine covariance matrix estimation in the asymptotic framework that the dimensionality p …
examine covariance matrix estimation in the asymptotic framework that the dimensionality p …
[HTML][HTML] Sparsistency and rates of convergence in large covariance matrix estimation
C Lam, J Fan - Annals of statistics, 2009 - ncbi.nlm.nih.gov
This paper studies the sparsistency and rates of convergence for estimating sparse
covariance and precision matrices based on penalized likelihood with nonconvex penalty …
covariance and precision matrices based on penalized likelihood with nonconvex penalty …
An empirical Bayes approach to inferring large-scale gene association networks
J Schäfer, K Strimmer - Bioinformatics, 2005 - academic.oup.com
Motivation: Genetic networks are often described statistically using graphical models (eg
Bayesian networks). However, inferring the network structure offers a serious challenge in …
Bayesian networks). However, inferring the network structure offers a serious challenge in …
[图书][B] Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis
MJ Daniels, JW Hogan - 2008 - taylorfrancis.com
Drawing from the authors' own work and from the most recent developments in the field,
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …
[HTML][HTML] Network exploration via the adaptive LASSO and SCAD penalties
Graphical models are frequently used to explore networks, such as genetic networks, among
a set of variables. This is usually carried out via exploring the sparsity of the precision matrix …
a set of variables. This is usually carried out via exploring the sparsity of the precision matrix …
[图书][B] High-dimensional covariance estimation: with high-dimensional data
M Pourahmadi - 2013 - books.google.com
Methods for estimating sparse and large covariance matrices Covariance and correlation
matrices play fundamental roles in every aspect of the analysis of multivariate data collected …
matrices play fundamental roles in every aspect of the analysis of multivariate data collected …