Library · Causal inference & statistics
What Bootstrap Resampling & Permutation got wrong, from 70 theses
Summary written by a model from the records below: The records document practical breakdowns and methodological limitations encountered when applying bootstrap resampling and permutation testing across diverse statistical problems. Across these settings, resampling strategies frequently suffer from coverage failures, computational bottlenecks, boundary inconsistencies, power loss, dependence violations, and numerical divergence. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 9 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Bootstrap intervals and variance estimators exhibit undercoverage or inaccurate dispersion estimates
Resampling techniques frequently underestimate true parameter variance or produce confidence intervals that fail nominal coverage rates in small samples and high-dimensional settings. In other evaluations, bootstrap variance estimators generate excessively wide intervals, over-correct estimation bias, or perform worse than analytical and cross-fitting baselines.
Tried and failed
pair and residual bootstrap applied to high-dimensional linear regression. Reason: consistently underestimates true parameter variance in overparametrized regimes even with optimal regularization
Theoretical characterization of uncertainty in high-dimensional machine learning · EPFL · PhD
Tried and failed
weighted likelihood bootstrap applied to normal mean posterior estimation. Outcome: did not generalise. Reason: produced non-normal posterior samples with variance independent of the observation noise scale
Objective Bootstrap Posterior distributions · UT Austin · PhD
Tried and failed
Discrete bootstrap resampling applied to continuous treatment effect estimation. Outcome: data insufficient. Reason: Poor support of continuous exposure in small samples
Causal Inference Methods To Evaluate Health Policies With Spillover · Penn · PhD
Lost to a baseline
Calibration-bootstrap method showed worse coverage accuracy (over-coverage on lower bounds, under-coverage on upper bounds) compared to predictive-distribution methods under heavy censoring
Prediction interval methods for reliability data · Iowa State · PhD
Lost to a baseline
Under large variance differences (σ2^2 = 2), Student's t-bootstrap CI width was substantially wider (AW 248.07) than asymptotic CI (AW 98.92) at sample sizes n1=n2=10.
Statistical methods and analysis with real-life data · University of Regina · Master's
Lost to a baseline
Linear regression selective MSE: bootstrap methods (Doubt-Var/Int) were beaten by standard PlugIn-REG and cross-fitting baselines.
Topics in Selective Prediction · Scuola Normale Superiore · PhD
Considered and rejected
Considered and rejected: Rejected fully parametric bootstrap for MSE estimation under informative sampling because it requires postulating a full distribution for survey weights rather than just sample moments.
Small area prediction problems under informative sampling and handling non-monotone missing data using a graphical approach · Iowa State · PhD
Considered and rejected
Considered and rejected: Bootstrapping with replacement for confidence intervals, rejected because it produces confidence intervals systematically biased below upper bounds
Boundary line methodology for yield gap analysis of farm systems · Nottingham · PhD
Considered and rejected
Considered and rejected: Rejected recommending adapted bootstrap for binary/proportion risk difference outcomes due to inflated Type I error (recommending cluster bootstrap instead).
An Adapted Bootstrap for Trials that Enroll until a Fixed Number of Events Are Observed: Application to the TREAT PARENTS Trial · Johns Hopkins · Master's
Tried and failed
nonparametric bootstrap rank confidence intervals applied to sparse multi-environment trial rankings. Outcome: did not generalise. Reason: Uniform interaction distributions degraded coverage under severe subsampling compared to bell-shaped distributions
Accounting for rank uncertainty in decision making for plant breeding · Iowa State · PhD
Tried and failed
wild bootstrap for nearest neighbor matching estimators applied to confidence interval estimation in high overlap. Reason: produced severe undercoverage and underestimated confidence interval width in nearest-neighbor matching
Causal Inference Methods for High-Resolution Data: Methodological Innovations for Estimating Treatment Effects in Complex Data Structures · Harvard · PhD
Tried and failed
bootstrap neural network ensemble for uncertainty quantification applied to trajectory prediction and control prediction intervals. Outcome: did not generalise. Reason: severe under-coverage resulting in extreme coverage width-based penalties
Scientific Machine Learning for Engineering Systems: Optimal Control, Prognostics, and Uncertainty Quantification · Virginia Tech · Doctorate
Tried and failed
bootstrap resampling on small sample sizes applied to side-channel leakage detection. Outcome: data insufficient. Reason: sample bias in restricted trace subsets failed to capture true statistical distribution peaks
Towards Comprehensive Side-channel Resistant Embedded Systems · Virginia Tech · Doctorate
Tried and failed
bootstrap bias correction applied to small area MSE estimation. Reason: standard bootstrap adjustment produced an over-correction of the estimation bias
Small area estimation and graphical model for complex surveys · Iowa State · PhD
Lost to a baseline
FQE (Bootstrap) produced tighter confidence intervals than BONDIC on Inverted-Pendulum, though it failed the nominal coverage rate across trials
Efficient and safe off-policy evaluation : from point estimation to interval estimation · UT Austin · PhD
Lost to a baseline
Percentile bootstrap CI coverage for A1,2 in high overlap (82.8%) was lower than chi-square-based intervals (90.3%).
New methods in home-range overlap and clustering · Iowa State · PhD
Lost to a baseline
Percentile and Wald bootstrap CI coverage drops below nominal 95% (to ~88-90%) under large sensitivity parameter values (α = 0.6) compared to smaller α values.
Estimation of Treatment Effects in the Presence of Irregular Assessment Times, Informative Missingness After ICU Discharge, and Interference · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Non-parametric biodiversity estimators (Chao1, Chao2, Jackknife, bootstrapping) rejected for biogeographic datasets where surveyed plot area is small (<2%) and rare species are largely unobserved.
Feature selection by Information Imbalance optimization: Clinics, molecular modeling and ecology · SISSA · PhD
Considered and rejected
Considered and rejected: Rejected non-parametric bootstrap for CI calculation because sample mean understates true r^2_ER
The estimation and explanation of tuning curves in intermediate visual areas · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected using bootstrapping for confidence interval calculation on manually measured bed thicknesses due to small sample sizes (N <= 8), using standard error instead.
The regional sedimentary record of Arabia Terra, Mars. · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Rejected test-inversion family bootstrapping methods because they cannot be used on non-parametric resampling.
Effect Sizes for Equivalence Testing: Incorporating the Equivalence Interval · York University · Master's
Permutation and bootstrap routines incur prohibitive computational overhead
Evaluating empirical null distributions or generating standard errors through repeated retraining creates severe computational bottlenecks on large cohorts and complex models. Consequently, researchers frequently reject full permutation or bootstrap schemes in favor of analytic approximations, moment-matching bounds, or precomputed distributions.
Tried and failed
Permutation-based pointwise contrast testing applied to functional data analysis contrasts. Outcome: too slow. Reason: Excessive computation time and conservative empirical test sizes
Functional Data Models for Raman Spectral Data and Degradation Analysis · Virginia Tech · Doctorate
Tried and failed
phylogenetic bootstrap inference on high-missingness sequence alignments applied to large reduced-representation genomic datasets. Outcome: too slow. Reason: bootstrapping datasets with 60% or 70% missing data was computationally intractable
Considered and rejected
Considered and rejected: Rejected Threshold-Free Cluster Enhancement (TFCE) permutation testing for the 13,680-subject UK Biobank cohort due to excessive computational burden.
Advances in statistical methods for large-scale binary-valued neuroimaging data · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected standard permutation testing across sample indices for large sample sizes in KMERF due to high computational overhead of retraining forests per permutation, choosing a chi-square test approximation instead.
Random Forest for Hypothesis Testing: Development and Application to Cancer Detection · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Rejected non-parametric local moderated t-statistic in cluster-based permutation inference due to intractable computational burden (10 hours for 1000 permutations on 40 cores).
Bayesian Methods for Analyzing Functional Magnetic Resonance Imaging Data · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Rejected using all possible permutations in DBFinder due to conservative FDR penalization in small sample sizes and computational cost, opting instead for balanced permutations.
Statistical Methods for Mobile Health and Genomics Data · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected permutation-based surrogate data testing for high-dimensional spike-field matrices due to extreme computational expense with increasing recording dimensionality, replacing it with an analytical Marchenko-Pastur bound.
Brain as a Complex System, harnessing systems neuroscience tools & notions for an empirical approach · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected random shuffling permutation for feature sensitivity due to excessive computational overhead from multiple shuffle iterations.
Fairness specification and repair for machine learning pipeline · Iowa State · PhD
Considered and rejected
Considered and rejected: Rejected standard permutation testing of retraining entire classifiers for thousands of iterations in MIGHT due to extreme computational inefficiency, adopting two-forest tree swapping instead.
Random Forest for Hypothesis Testing: Development and Application to Cancer Detection · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Rejected running JLIM null distributions separately for each individual cell in scJLIM due to prohibitive computational expense; replaced with a single precomputed null distribution of N=100,000 permutations shared across cells
Computational methods for dissecting multicellular mechanisms of complex diseases · MIT · PhD
Resampling schemes become statistically inconsistent near parameter boundaries and non-smooth metrics
Standard bootstrap methods break down when applied to directional derivatives in optimal transport distances, parameter boundaries, or post-selection estimators such as the LASSO. Under these non-regular conditions, empirical resamples exhibit covariance drift, over-reject significance levels, or fail asymptotic consistency.
Tried and failed
naive two-sample bootstrap for Wasserstein distance applied to two-sample hypothesis testing. Reason: inconsistent for null limit distribution due to extra covariance drift, requiring pooled distribution resampling
Robust and Geometry-Aware Machine Learning Using Optimal Transport · Cornell · PhD
Tried and failed
standard bootstrap confidence intervals applied to constrained maximum likelihood estimation. Reason: finite samples frequently contained zero false negatives causing boundary issues
Essays on Data Science: Computational Measurement for Learning and Teaching · Harvard · PhD
Tried and failed
m-out-of-n subsampling and bootstrap inference applied to optimal transport inference. Reason: exhibited poor size control, over-rejecting significance levels and requiring impractically large sample sizes to converge
Essays on the US Mortgage Market · Harvard · PhD
Tried and failed
naive empirical bootstrap applied to sliced Wasserstein distance hypothesis testing. Reason: inconsistent when the set of optimal potentials is non-unique under the alternative hypothesis
Statistical Inference for Regularized Optimal Transport · Cornell · PhD
Tried and failed
standard nonparametric bootstrap applied to empirical max-sliced Wasserstein distance. Reason: non-linear directional Hadamard derivatives cause bootstrap inconsistency
Statistical Inference for Regularized Optimal Transport · Cornell · PhD
Tried and failed
random rotation bootstrap on spherical point patterns applied to cross summary statistics null envelopes. Reason: replicates collapse to zero variance at maximum spherical distance pi due to geometric constraints
Analysis of spatial point patterns on surfaces · Imperial · PhD
Tried and failed
standard nonparametric bootstrap applied to max-sliced Wasserstein and Gromov-Wasserstein distances. Reason: nonlinearity of the directional derivative makes the bootstrap statistically inconsistent
Computational and Statistical Properties of Optimal Transport-Based Distances · Cornell · PhD
Considered and rejected
Considered and rejected: Residual bootstrap for variance estimation in bounded Tobit longitudinal data was rejected in favor of random weighting due to repeated sampling of extreme boundary values.
Statistical Models for Identification of Treatment-sensitive Subgroups Based on Longitudinal Outcomes in Clinical Trials · Queen's University · PhD
Considered and rejected
Considered and rejected: Rejected using standard multinomial bootstrap for inference on simplex-boundary parameters due to asymptotic failure.
Measurement Error in Microbiome Sequencing Experiments: Statistical and Scientific Considerations · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected re-fitting the propensity score using post-LASSO within each bootstrap resample because bootstrap is inconsistent for LASSO estimators.
Propensity Score Methods For Causal Subgroup Analysis · Duke · PhD
Considered and rejected
Considered and rejected: Rejected bias correction in the parametric bootstrap for selection coefficient confidence intervals due to optimization instability when s is near zero.
Statistical Inference Using Identity-by-Descent Segments: Perspectives on Recent Positive Selection · University of Washington · PhD
Tried and failed
Wald and bootstrap interval estimators applied to constrained network parameters under RDS. Reason: Failed to achieve valid nominal coverage, producing under 50% coverage for bounded partnership probability parameters
Statistical Methods for Sparse Network Data and Mechanistic Models of Infectious Disease: Latent Space and Likelihood-Free Inference · Harvard · PhD
Permutation and resampling tests suffer from low power and distorted null distributions
Permutation and bootstrap hypothesis tests often exhibit lower statistical power than parametric baselines and struggle to control false discovery rates in small or skewed samples. Furthermore, permutation tests cannot easily adjust for confounding covariates and backward permutation rankings can invert predictor importance.
Lost to a baseline
The multivariate JA test with wild bootstrap exhibits lower empirical power under heteroskedasticity than under Gaussian or t(5) error distributions.
Three Essays in Econometrics · Carleton University · PhD
Lost to a baseline
Pivotal bootstrap testing has lower statistical power compared to oracle Gaussian testing under identical sample sizes.
TOPICS IN MODERN REGRESSION MODELING · Cornell · PhD
Tried and failed
exact permutation testing for differential region detection applied to small sample genomic data. Outcome: no signal. Reason: extreme group swap permutations skewed the null false discovery rate distribution in small samples
Statistical Methods for Mobile Health and Genomics Data · Harvard · PhD
Tried and failed
variance stabilization with permutation testing applied to differential ChIP-seq signal detection. Reason: elevated false discovery rate when detecting weak induced effect sizes
Quantitative analysis of ChIP-seq signals and transcriptomes · Georgia Tech · PhD
Lost to a baseline
Sim-5 (classification permutation test comparison): Classification permutation test using random forests (package cpt) achieved substantially lower power to detect mean shifts across δ than GFKS-1, GFKS-2, adjusted KS, and adjusted t-tests.
Contributions To Multivariate Matching In Observational Studies · Penn · PhD
Lost to a baseline
PGLS was completely outperformed by standard DESeq2 due to non-normal count distributions, resulting in 90-100% false discovery rates under permutation
Macroevolution of Gene Expression in Passerine Birds · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected permutation tests (pre-network datastream permutations) in favor of parametric regression GLMMs because permutations do not control effect sizes for confounds.
Individual Factors Predicting the Disappearance and Reproductive Success of Vervet Monkeys (Chlorocebus pygerythrus) · York University · Master's
Tried and failed
bootstrap inclusion probabilities for sparse regression selection applied to governing equation discovery from time series. Outcome: no signal. Reason: inclusion probabilities failed to clearly separate true model terms from false positives
Parameter estimation and inference for nonlinear dynamical systems · Cornell · PhD
Lost to a baseline
In small sample and unbalanced strata settings (n10 < 10), the Louis estimator with fixed weight (q0=0.7) exhibited slightly higher power than the bootstrapped optimal-weight estimator.
Assessing treatment benefit in the presence of placebo response using the Sequential Parallel Comparison Design · Boston University · PhD
Tried and failed
backward permutation feature importance applied to ensemble postprocessing neural networks. Reason: produced inverted, unphysical predictor rankings conflicting with forward permutation tests
Leveraging Artificial Intelligence and Numerical Weather Prediction to Build Custom Smart Home Software · Texas Tech · PhD
Lost to a baseline
Under Gaussian distributions, all rank-based distance covariance tests (Wilcoxon scores and normal scores) and permutation tests were uniformly outperformed by the parametric Gaussian Likelihood Ratio Test.
Distribution-Free Consistent Tests of Independence via Marginal and Multivariate Ranks · University of Washington · PhD
Unstructured resampling breaks down under temporal, spatial, or network dependence
Naive permutation and independent bootstrap resampling violate exchangeability assumptions when applied to time series, spatial coordinates, or network structures. These failures lead to broken temporal autocorrelations, severely underestimated standard errors under network dependence, and inflated Type I error rates.
Considered and rejected
Considered and rejected: Rejected standard permutation testing due to non-exchangeability of correlated error structures and severe computational burden, opting instead for residual projection and Pearson type III moment-matching approximations
Generalization of kernel machine methods for association testing of multi-omics data · University of Washington · PhD
Tried and failed
moving block bootstrap with self-normalization statistic applied to GARCH time series mean hypothesis testing. Outcome: worse than baseline. Reason: failed to improve coverage accuracy and degraded performance under several GARCH(1,1) conditions
A NEW APPROACH TO SELF-NORMALIZATION & BANK PERFORMANCE ANALYSIS USING MULTI-TASK GAUSSIAN PROCESS MODEL · Cornell · PhD
Tried and failed
unit-level bootstrap variance estimation applied to bipartite network causal inference. Reason: outcome-level resampling fails to account for network dependence across intervention units, severely underestimating standard errors
Interpretable random structure for non-standard data with applications to biomedical and social science studies · UT Austin · PhD
Tried and failed
multi-criteria constrained bootstrap resampling applied to multivariate time series generation. Outcome: did not converge. Reason: insufficient pool of coupled multivariate historical data to satisfy all constraints simultaneously
Considered and rejected
Considered and rejected: Standard/basic bootstrap rejected for time-series panel data in favor of block bootstrap because standard bootstrap fails to capture time-dependence structure.
Educational Disparities in Chronic Pain and Life Expectancy: Gaps and Pathways · University at Buffalo · PhD
Considered and rejected
Considered and rejected: Discrete bootstrapping across independent observations, rejected due to poor support of the continuous exposure and failure to respect spatiotemporal correlation
Advancing Data Science Methods for Environmental Health Policy Design and Evaluation · Harvard · PhD
Considered and rejected
Considered and rejected: Bootstrap confidence interval methods (Moving Block / Semiparametric); rejected due to insufficient contiguous record lengths in sparse SWOT Cal/Val data.
Spatiotemporal tidal prediction and analysis through physics-informed machine learning · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected standard SHAP and permutation explainability methods directly on raw temporal series because permuting individual time steps breaks temporal autocorrelations and trajectory dependencies.
Survival Analysis Using Machine Learning for Longitudinal, Multimodal, and High-dimensional Data for Applications in Cardiology · Johns Hopkins · PhD
Tried and failed
unstructured permutation testing of temporal labels applied to temporally imbalanced sequence data. Reason: temporal density imbalances across sample periods generated artificially inflated statistical significance
Studies in Bacterial Genome Dynamics · Harvard · PhD
Tried and failed
Empirical copula permutation tests for correlation homogeneity applied to Testing heterogeneous correlation structure. Reason: Failed to control Type I error under high overall sample correlation.
Testing for associations in a heterogeneous population · Texas Tech · PhD
Resample generation causes numerical instability, singular designs, and optimization divergence
Repeated sampling of discrete categories, bounded observations, or small clusters frequently produces empty cells, dropped covariate levels, and singular design matrices. These degenerate resamples lead to optimization divergence, blown-up standard errors, and negative variance estimates.
Tried and failed
bootstrap variance estimation after regression calibration applied to survival models with measurement error. Outcome: unstable. Reason: a significant fraction of bootstrap resamples produced extreme coefficient estimates, blowing up standard error
Tried and failed
Parametric bootstrap simulation of dispersion parameters applied to Laplace-approximated mixed-effects models. Outcome: did not converge. Reason: High variance in simulated parameters caused numerical divergence during iterative optimization
Methods for spatial hierarchical generalized linear mixed models · Iowa State · PhD
Tried and failed
parametric double bootstrap mean squared error estimation applied to small area estimation under non-linear models. Outcome: unstable. Reason: produced negative variance estimates preventing confidence interval construction
Small area prediction problems under informative sampling and handling non-monotone missing data using a graphical approach · Iowa State · PhD
Tried and failed
stratified cluster bootstrap sampling applied to complex survey-weighted longitudinal data. Outcome: unstable. Reason: produced unstable, biased, and asymmetric sampling distributions
Intimate Partner Violence and Dementia Risk: Challenges and Opportunities for Longitudinal Studies of Aging · Harvard · PhD
Lost to a baseline
DIRECT + Powell's method resulted in broader parameter estimate confidence intervals and produced large numbers of outliers on bootstrapped synthetic data compared to Baum-Welch (e.g., target detachment rate fell outside the entire outlier range).
Computational modeling of protein fluorosequencing · UT Austin · PhD
Considered and rejected
Considered and rejected: Decided against non-parametric bootstrapping for calculating mediation indirect effects due to convergence failures and inflated error estimates
Identifying Mechanisms of Change in Dialectical Behavior Therapy for Substance Use Disorders (DBT-SUD): A Mixed Methods Examination of Mediators of Substance Use Reduction · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected traditional resampling bootstrap because cells with many fixed effects became empty, choosing Dirichlet random weighting instead
Essays in Labor Economics · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Decided against pair bootstrapping because resampling clusters causes covariates to lack variance/drop levels and results in varying sample sizes with small cluster counts.
Cluster wild bootstrapping to handle dependent effect sizes in meta-analysis with small number of studies · UT Austin · PhD
Tried and failed
adaptive ensemble kernels in bootstrap testing applied to nonlinear hypothesis testing. Outcome: unstable. Reason: adaptive estimation caused unstable test statistics and underestimated the alternative kernel compared to fixed alternatives
A Gaussian-process Framework for Nonlinear Statistical Inference using Modern Machine Learning Models · Harvard · PhD
Considered and rejected
Considered and rejected: Nonparametric pairs (x-y) bootstrap was rejected because it frequently yields singular design matrices during resampling in simulations.
Unified Framework For Post-Selection Inference · Penn · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Extend iterated 2SLS outlier test statistics to non-normal reference distributions using Student-t and empirical bootstrap distributions. Blocker: None
Essays on outlier and break detection · Oxford · PhD
Left open
Derive the asymptotic distribution theory for sign-preserved spatially varying coefficient estimators to eliminate the need for bootstrap inference. Blocker: Requires advanced mathematical statistics theory rather than software engineering.
Generalized, quantile and constrained nonparametric regression for spatial data · Iowa State · PhD
Left open
Evaluate selection model extensions incorporating robust variance estimation and bootstrapping for dependent effect sizes in meta-analysis via simulation. Blocker: None
Evaluating selective reporting methods in meta-analysis : considering dependent effect sizes when estimating adjusted effect sizes · UT Austin · PhD
Left open
Prove that the exponentially tilted Bayesian bootstrap posterior is the noninformative limit of the Kitamura-Otsu formulation as base measure approaches zero. Blocker: None
Weighting and moment conditions in Bayesian inference · Cambridge · PhD
Left open
Implement a full bootstrap routine over the multi-step structural estimation procedure to compute standard errors accounting for earlier-stage estimation error. Blocker: Requires access to the thesis's specific empirical bidding datasets and estimation codebase.
Essays in Environmental and Healthcare Market Design · MIT · PhD
Left open
Extend the bootstrap aggregation (bagging) acquisition function procedure to fully Bayesian MCMC inference for Deep Gaussian Processes. Blocker: None
Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing · Virginia Tech · Doctorate
Left open
Perform conformal bootstrap using mixed phi, s, t correlators at higher derivative truncation Lambda to determine the O(3) model fate in 2<d<3. Blocker: None
Left open
Implement Chung 2019 transposition permutations to reduce the permutation test cost for energy distance goodness-of-fit tests on manifolds. Blocker: None
Goodness of fit tests for object data · Texas Tech · PhD
Left open
Develop fast analytical approximations for small-sample (N < 20) distance correlation hypothesis testing to replace permutation tests. Blocker: None
Multiscale Statistical Hypothesis Testing for k-Sample Graph Inference · Johns Hopkins · Master's
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Sambit Panda · Johns Hopkins · PhD · 2025
Random Forest for Hypothesis Testing: Development and Application to Cancer Detection - Ritwik Sadhu · Cornell · PhD · 2024
Statistical Inference for Regularized Optimal Transport - Yanghyeon Cho · Iowa State · PhD · 2023
Small area prediction problems under informative sampling and handling non-monotone missing data using a graphical approach - Matthew Quinn · Harvard · PhD · 2022
Statistical Methods for Mobile Health and Genomics Data - Emma Grace Crenshaw · Harvard · PhD · 2026
Statistical Methods for Sparse Network Data and Mechanistic Models of Infectious Disease: Latent Space and Likelihood-Free Inference - Arya Kaul · Harvard · PhD · 2026
Studies in Bacterial Genome Dynamics - Jostein Alf Barry-Straume · Virginia Tech · Doctorate · 2026
Scientific Machine Learning for Engineering Systems: Optimal Control, Prognostics, and Uncertainty Quantification - Sloan Nietert · Cornell · PhD · 2025
Robust and Geometry-Aware Machine Learning Using Optimal Transport - Gabriel Rioux · Cornell · PhD · 2025
Computational and Statistical Properties of Optimal Transport-Based Distances - Zachary Himmelsbach · Harvard · PhD · 2025
Essays on Data Science: Computational Measurement for Learning and Teaching - Xiang Meng · Harvard · PhD · 2025
Causal Inference Methods for High-Resolution Data: Methodological Innovations for Estimating Treatment Effects in Complex Data Structures - Alexandria DiGiacomo · Harvard · PhD · 2025
Macroevolution of Gene Expression in Passerine Birds
Checking a claim in this area?
We can run the same search on any method or claim. If nothing turns up, we will say so, and that proves nothing on its own.