The following pages link to Mark J. Van der Laan (Q62434):
Displaying 50 items.
- Second-Order Inference for the Mean of a Variable Missing at Random (Q6267790) (← links)
- Finite Sample Inference for Targeted Learning (Q6290705) (← links)
- Uniform Consistency of the Highly Adaptive Lasso Estimator of Infinite Dimensional Parameters (Q6291503) (← links)
- Fast rates for empirical risk minimization over c\`adl\`ag functions with bounded sectional variation norm (Q6322482) (← links)
- Sufficient and insufficient conditions for the stochastic convergence of Ces\`{a}ro means (Q6348986) (← links)
- Higher Order Targeted Maximum Likelihood Estimation (Q6358380) (← links)
- Sequential causal inference in a single world of connected units (Q6358553) (← links)
- Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes (Q6366900) (← links)
- Higher Order Spline Highly Adaptive Lasso Estimators of Functional Parameters: Pointwise Asymptotic Normality and Uniform Convergence Rates (Q6424918) (← links)
- Estimating conditional hazard functions and densities with the highly-adaptive lasso (Q6530909) (← links)
- Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation (Q6589248) (← links)
- Rachael V. Phillips and Mark J. van der Laan's contribution to the discussion of `Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes (Q6600831) (← links)
- Personalized online ensemble machine learning with applications for dynamic data streams (Q6617455) (← links)
- Targeted learning with daily EHR data (Q6624675) (← links)
- Adaptive sequential surveillance with network and temporal dependence (Q6625229) (← links)
- Defining and estimating effects in cluster randomized trials: a methods comparison (Q6626873) (← links)
- Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials (Q6628354) (← links)
- Targeted estimation of nuisance parameters to obtain valid statistical inference (Q6632692) (← links)
- Targeted estimation of binary variable importance measures with interval-censored outcomes (Q6632695) (← links)
- Statistical inference for data adaptive target parameters (Q6632717) (← links)
- Testing the relative performance of data adaptive prediction algorithms: a generalized test of conditional risk differences (Q6632726) (← links)
- A case study of the impact of data-adaptive versus model-based estimation of the propensity scores on causal inferences from three inverse probability weighting estimators (Q6632727) (← links)
- Optimal spatial prediction using ensemble machine learning (Q6632730) (← links)
- AUC-maximizing ensembles through metalearning (Q6632731) (← links)
- Doubly robust and efficient estimation of marginal structural models for the hazard function (Q6632733) (← links)
- Optimal individualized treatments in resource-limited settings (Q6632735) (← links)
- Super-learning of an optimal dynamic treatment rule (Q6632736) (← links)
- Second-order inference for the mean of a variable missing at random (Q6632737) (← links)
- One-step targeted minimum loss-based estimation based on universal least favorable one-dimensional submodels (Q6632738) (← links)
- A machine learning-based approach for estimating and testing associations with multivariate outcomes (Q6636001) (← links)
- Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator (Q6636047) (← links)
- Commentary: Big data, small sample (Q6636147) (← links)
- A generally efficient targeted minimum loss based estimator based on the highly adaptive Lasso (Q6636149) (← links)
- Marginal structural models with counterfactual effect modifiers (Q6636166) (← links)
- The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions (Q6636222) (← links)
- Estimators for the value of the optimal dynamic treatment rule with application to criminal justice interventions (Q6636223) (← links)
- Efficient estimation of pathwise differentiable target parameters with the undersmoothed highly adaptive lasso (Q6636224) (← links)
- Statistics, philosophy, and health: the SMAC 2021 webconference (Q6636225) (← links)
- Double robust efficient estimators of longitudinal treatment effects: comparative performance in simulations and a case study (Q6637206) (← links)
- Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems (Q6637420) (← links)
- Assessing the causal effect of policies: an example using stochastic interventions (Q6637421) (← links)
- One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves (Q6637860) (← links)
- Correction to: ``Nonparametric efficient causal mediation with intermediate confounders'' (Q6637903) (← links)
- Statistical inference for variable importance (Q6644489) (← links)
- Application of a variable importance measure method (Q6644495) (← links)
- Choice of monitoring mechanism for optimal nonparametric functional estimation for binary data (Q6644496) (← links)
- Estimating a survival distribution with current status data and high-dimensional covariates (Q6644498) (← links)
- Targeted maximum likelihood learning (Q6644500) (← links)
- Empirical efficiency maximization: improved locally efficient covariate adjustment in randomized experiments and survival analysis (Q6644510) (← links)
- Rejoinder to Tan (Q6644520) (← links)
