Cover Image

Quantifying uncertainty in spikes estimated from calcium imaging data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Yiqun T., Jewell, Sean W., Witten, Daniela M.
Format:	Preprint
Published:	2021
Subjects:	Methodology Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Valid F-screening in linear regression
by: McGough, Olivia, et al.
Published: (2025)

Blinded sample size re-estimation accounting for uncertainty in mid-trial estimation
by: Maeda, Hirotada, et al.
Published: (2026)

Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest
by: Kabata, Daijiro, et al.
Published: (2025)

Treatment bootstrapping: A new approach to quantify uncertainty of average treatment effect estimates
by: Li, Jing
Published: (2023)

Direct covariance matrix estimation with compositional data
by: Molstad, Aaron J., et al.
Published: (2022)

Quantifying How Much Has Been Learned from a Research Study
by: Mikhaeil, Jonas M., et al.
Published: (2025)

Bond strength uncertainty quantification via confidence intervals for nondestructive evaluation of bonded composites
by: Stanley, Michael C., et al.
Published: (2025)

Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes
by: Sharma, Bharat, et al.
Published: (2025)

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
by: Papacharalampous, Georgia, et al.
Published: (2024)

Overcoming model uncertainty -- how equivalence tests can benefit from model averaging
by: Hagemann, Niklas, et al.
Published: (2024)

A time warping model for seasonal data with application to age estimation from narwhal tusks
by: Reiter, Lars Nørtoft, et al.
Published: (2024)

JCGM 101-compliant uncertainty evaluation using virtual experiments
by: Hughes, Finn, et al.
Published: (2024)

Automated threshold selection and associated inference uncertainty for univariate extremes
by: Murphy, Conor, et al.
Published: (2023)

Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome
by: Wolock, Charles J., et al.
Published: (2024)

Quantifying patient and neighborhood risks for stillbirth and preterm birth in Philadelphia with a Bayesian spatial model
by: Balocchi, Cecilia, et al.
Published: (2021)

Quantifying the HIV reservoir with dilution assays and deep viral sequencing
by: Lotspeich, Sarah C., et al.
Published: (2023)

Effects of model misspecification on small area estimators
by: Chen, Yuting, et al.
Published: (2024)

Local graph estimation with pathwise false discovery control
by: Melikechi, Omar, et al.
Published: (2025)

Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation
by: Dashti, S. Ghazaleh, et al.
Published: (2021)

Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
by: Zhao, Pan, et al.
Published: (2026)

A Bayesian joint model of multiple nonlinear longitudinal and competing risks outcomes for dynamic prediction in multiple myeloma: joint estimation and corrected two-stage approaches
by: Alvares, Danilo, et al.
Published: (2024)

Parameter estimation of the four-parameter Harris extended Weibull distribution with applications to real-life data
by: Chaturvedi, Prithul, et al.
Published: (2025)

Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression
by: Pai, Nidhi, et al.
Published: (2026)

A Sensitivity Analysis Framework for Quantifying Confidence in Decisions in the Presence of Data Uncertainty
by: Wadekar, Adway S., et al.
Published: (2025)

Hierarchical models for small area estimation using zero-inflated forest inventory variables: comparison and implementation
by: White, Grayson W., et al.
Published: (2025)

Model-assisted analysis of covariance estimators for stepped wedge cluster randomized experiments
by: Chen, Xinyuan, et al.
Published: (2023)

Rank-based methods for estimating landmark win probability in longitudinal randomized controlled trials with missing data
by: Zou, Guangyong, et al.
Published: (2026)

Coarsened data in small area estimation: a Bayesian two-part model for mapping smoking behaviour
by: Gardini, Aldo, et al.
Published: (2026)

Ridge, lasso, and elastic-net estimations of the modified Poisson and least-squares regressions for binary outcome data
by: Kitano, Takahiro, et al.
Published: (2024)

A continuous-time Markov chain framework for population size estimation from multi-list data: accounting for absorbing lists and asymmetric interactions
by: Schaller, Ophélie, et al.
Published: (2026)

Robust Bayesian causal estimation for causal inference in medical diagnosis
by: Basu, Tathagata, et al.
Published: (2024)

A comparison between copula-based, mixed model, and estimating equation methods for regression of bivariate correlated data
by: Sareff-Hibbert, Aydin, et al.
Published: (2024)

Extreme value methods for estimating rare events in Utopia
by: André, L. M., et al.
Published: (2023)

Optimized imaging prefiltering for enhanced image segmentation
by: Vallejos, Ronny, et al.
Published: (2025)

Feature aware covariance estimation, with application to mixtures of chemical exposures
by: Bersson, Elizabeth, et al.
Published: (2025)

Estimating the duration of RT-PCR positivity for SARS-CoV-2 from doubly interval censored data with undetected infections
by: Blake, Joshua, et al.
Published: (2025)

Reliability of stochastic capacity estimates
by: Mikolasek, Igor
Published: (2026)

Evaluating the impact of outcome delay on the efficiency of sample size re-estimation
by: Mukherjee, Aritra, et al.
Published: (2026)

Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function
by: Murphy-Barltrop, C. J. R., et al.
Published: (2023)

When Bayes goes bad: Weakly-regularized covariate adjustment leads to a biased estimate of prevalence
by: Kuh, Swen, et al.
Published: (2026)