Inferactive data analysis

Bi, Nan; Markovic, Jelena; Xia, Lucy; Taylor, Jonathan

doi:10.1111/sjos.12425

Cited by 6 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Relative to other inference paradigms, selective inference offers the flexibility of choosing the model under which to conduct inference after examining the data. Classical inference procedures are, of course, rendered invalid when the model is selected only after observing the outcome of interest, and tools for selective inference provide a rigorous roadmap for overcoming this daunting challenge (Bi et al, 2020;Benjamini, 2020).…”

Section: Introductionmentioning

confidence: 99%

Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging

Panigrahi¹,

Stewart²,

Sripada³

et al. 2022

Preprint

View full text Add to dashboard Cite

Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately. Despite the potential of multi-task learning to yield more powerful inference than single-task alternatives, prior work in this area has largely omitted uncertainty quantification. Our focus in this paper is a common multi-task problem in neuroimaging, where the goal is to understand the relationship between multiple cognitive task scores (or other subject-level assessments) and brain connectome data collected from imaging. We propose a framework for selective inference to address this problem, with the flexibility to: (i) jointly identify the relevant covariates for each task through a sparsity-inducing penalty, and (ii) conduct valid inference in a model based on the estimated sparsity structure. Our framework offers a new conditional procedure for inference, based on a refinement of the selection event that yields a tractable selection-adjusted likelihood. This gives an approximate system of estimating equations for maximum likelihood inference, solvable via a single convex optimization problem, and enables us to efficiently form confidence intervals with approximately the correct coverage. Applied to both simulated data and data from the Adolescent Cognitive Brain Development (ABCD) study, our selective inference methods yield tighter confidence intervals than commonly used alternatives, such as data splitting. We also demonstrate through simulations that multi-task learning with selective inference can more accurately recover true signals than single-task methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging

Panigrahi¹,

Stewart²,

Sripada³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Identifying research competencies among pre-service teachers

Sumarni¹,

Akhyar²,

Nizam³

et al. 2023

The 3rd International Conference on Science Education and Technology (Icoseth 2021)

View full text Add to dashboard Cite

On selection and conditioning in multiple testing and selective inference

Goeman,

Solari

2023

Biometrika

View full text Add to dashboard Cite

We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven collection of hypotheses is chosen from some large universe of hypotheses. Subsequently, inference takes place within this data-driven collection, conditioned on the information that was used for the selection. Examples of such methods include basic data splitting, as well as modern data carving methods and post-selection inference methods for lasso coefficients based on the polyhedral lemma. In this paper, we adopt a holistic view on such methods, considering the selection, conditioning, and final error control steps together as a single method. From this perspective, we demonstrate that multiple testing methods defined directly on the full universe of hypotheses are always at least as powerful as selective inference methods based on selection and conditioning. This result holds true even when the universe is potentially infinite and only implicitly defined, such as in the case of data splitting. We give general theory and intuitions before investigating in detail several case studies where a shift to a non-selective or unconditional perspective can yield a power gain.

show abstract

Inferactive data analysis

Cited by 6 publications

References 43 publications

Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging

Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging

Identifying research competencies among pre-service teachers

On selection and conditioning in multiple testing and selective inference

Contact Info

Product

Resources

About