Bayesian analysis of cross-sectional networks: A tutorial in R and JASP
Network psychometrics is a new direction in psychological research that conceptualizes
psychological constructs as systems of interacting variables. In network analysis, variables …
psychological constructs as systems of interacting variables. In network analysis, variables …
Statistical methods in integrative genomics
Statistical methods in integrative genomics aim to answer important biology questions by
jointly analyzing multiple types of genomic data (vertical integration) or aggregating the …
jointly analyzing multiple types of genomic data (vertical integration) or aggregating the …
Sparse and compositionally robust inference of microbial ecological networks
16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide
snapshots of microbial communities, revealing phylogeny and the abundances of microbial …
snapshots of microbial communities, revealing phylogeny and the abundances of microbial …
mgm: Estimating time-varying mixed graphical models in high-dimensional data
J Haslbeck, LJ Waldorp - arXiv preprint arXiv:1510.06871, 2015 - arxiv.org
We present the R-package mgm for the estimation of k-order Mixed Graphical Models
(MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These …
(MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These …
[图书][B] Foundations of linear and generalized linear models
A Agresti - 2015 - books.google.com
A valuable overview of the most important ideas and results in statistical modeling Written by
a highly-experienced author, Foundations of Linear and Generalized Linear Models is a …
a highly-experienced author, Foundations of Linear and Generalized Linear Models is a …
On asymptotically optimal confidence regions and tests for high-dimensional models
On asymptotically optimal confidence regions and tests for high-dimensional models Page 1
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …
[PDF][PDF] Confidence intervals and hypothesis testing for high-dimensional regression
A Javanmard, A Montanari - The Journal of Machine Learning Research, 2014 - jmlr.org
Fitting high-dimensional statistical models often requires the use of non-linear parameter
estimation procedures. As a consequence, it is generally impossible to obtain an exact …
estimation procedures. As a consequence, it is generally impossible to obtain an exact …
Back to the basics: Rethinking partial correlation network methodology
DR Williams, P Rast - British Journal of Mathematical and …, 2020 - Wiley Online Library
The Gaussian graphical model (GGM) is an increasingly popular technique used in
psychology to characterize relationships among observed variables. These relationships are …
psychology to characterize relationships among observed variables. These relationships are …
High-dimensional inference: confidence intervals, p-values and R-software hdi
We present a (selective) review of recent frequentist high-dimensional inference methods for
constructing p-values and confidence intervals in linear and generalized linear models. We …
constructing p-values and confidence intervals in linear and generalized linear models. We …
Identifiability of Gaussian structural equation models with equal error variances
J Peters, P Bühlmann - Biometrika, 2014 - academic.oup.com
We consider structural equation models in which variables can be written as a function of
their parents and noise terms, which are assumed to be jointly independent. Corresponding …
their parents and noise terms, which are assumed to be jointly independent. Corresponding …