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Found 22 papers, 9 papers with code
Revisiting inference after prediction
Recent work has focused on the very common practice of prediction-based inference: that is, (i) using a pre-trained machine learning model to predict an unobserved response variable, and then (ii) conducting inference on the association between that predicted response and some covariates.
Generalized Data Thinning Using Sufficient Statistics
A recent paper showed that for some well-known natural exponential families, $X$ can be "thinned" into independent random variables $X^{(1)}, \ldots, X^{(K)}$, such that $X = \sum_{k=1}^K X^{(k)}$.
Data thinning for convolution-closed distributions
We propose data thinning, an approach for splitting an observation into two or more independent parts that sum to the original observation, and that follow the same distribution as the original observation, up to a (known) scaling of a parameter.
Inferring independent sets of Gaussian variables after thresholding correlations
Our proposed test properly accounts for the fact that the set of variables is selected from the data, and thus is not overly conservative.
Selective Inference for Hierarchical Clustering
Classical tests for a difference in means control the type I error rate when the groups are defined a priori.
Testing for Association in Multi-View Network Data
To answer this question, we extend the stochastic block model for a single network view to the two-view setting, and develop a new hypothesis test for the null hypothesis that the latent community memberships in the two data views are independent.
Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity
Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations.
Modeling microbial abundances and dysbiosis with beta-binomial regression
Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem.
Methodology
Are Clusterings of Multiple Data Views Independent?
However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views.
Robust Sparse Reduced Rank Regression in High Dimensions
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise.
Fast Nonconvex Deconvolution of Calcium Imaging Data
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously.
Methodology Neurons and Cognition Applications
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference
In this paper, we consider a simple and very na\"{i}ve two-step procedure for this task, in which we (i) fit a lasso model in order to obtain a subset of the variables, and (ii) fit a least squares model on the lasso-selected set.
Exact Spike Train Inference Via $\ell_0$ Optimization
For each neuron, a fluorescence trace is measured; this can be seen as a first-order approximation of the neuron's activity over time.
Applications Neurons and Cognition
Convex Modeling of Interactions with Strong Heredity
We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity.
Fused Lasso Additive Model
We consider the problem of predicting an outcome variable using $p$ covariates that are measured on $n$ independent observations, in the setting in which flexible and interpretable fits are desirable.
Sure Screening for Gaussian Graphical Models
We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting.
Selection Bias Correction and Effect Size Estimation under Dependence
Many authors have proposed methods to reduce the effects of selection bias under the assumption that the naive estimates of the effect sizes are independent.
Learning Graphical Models With Hubs
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes.
Inference in High Dimensions with the Penalized Score Test
Further, when an $\ell_2$ penalty is used, the test corresponds precisely to a score test in a mixed effects model, in which the effects of all but one feature are assumed to be random.
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models
We begin by introducing a surprising connection between the graphical lasso and hierarchical clustering: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) an l1-penalized log likelihood is maximized on the subset of variables within each connected component.
Node-Based Learning of Multiple Gaussian Graphical Models
We consider estimation under two distinct assumptions: (1) differences between the K networks are due to individual nodes that are perturbed across conditions, or (2) similarities among the K networks are due to the presence of common hub nodes that are shared across all K networks.
Structured Learning of Gaussian Graphical Models
We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions.
