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Mechanistic insight into the borrowing hydrogen reaction catalysed by Pd MLC catalyst: Unveiling the ligand-to-ligand hydrogen transfer pathway
Org. Chem. Front., 2024, Accepted ManuscriptDOI: 10.1039/D4QO00688G, Research ArticleLan-Yu Li, Cheng HouMetal-ligand bifunctional catalysis plays a pivotal role in various catalytic hydrogenation and dehydrogenation processes.
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
arXiv:2310.12842v2 Announce Type: replaceAbstract: In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions.
On the Properties and Estimation of Pointwise Mutual Information Profiles
arXiv:2310.10240v2 Announce Type: replaceAbstract: The pointwise mutual information profile, or simply profile, is the distribution of pointwise mutual information for a given pair of random variables.
Improving Neural Additive Models with Bayesian Principles
arXiv:2305.16905v4 Announce Type: replaceAbstract: Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks.
MRCpy: A Library for Minimax Risk Classifiers
arXiv:2108.01952v4 Announce Type: replaceAbstract: Libraries for supervised classification have enabled the wide-spread usage of machine learning methods.
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
arXiv:2401.16567v2 Announce Type: replaceAbstract: The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior.
The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-Data Applications
arXiv:2309.03952v5 Announce Type: replaceAbstract: The Causal Roadmap outlines a systematic approach to asking and answering questions of cause-and-effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results.
Visibility graph-based covariance functions for scalable spatial analysis in non-convex domains
arXiv:2307.11941v3 Announce Type: replaceAbstract: We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water.
Causal Inference for Balanced Incomplete Block Designs
arXiv:2405.19312v1 Announce Type: newAbstract: Researchers often turn to block randomization to increase the precision of their inference or due to practical considerations, such as in multi-site trials.
Covariate Shift Corrected Conditional Randomization Test
arXiv:2405.19231v1 Announce Type: newAbstract: Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable $Y$ from a treatment variable $X$, conditioning on a set of confounders $Z$.
L-Estimation in Instrumental Variables Regression for Censored Data in Presence of Endogeneity and Dependent Errors
arXiv:2405.19145v1 Announce Type: newAbstract: In this article, we propose L-estimators of the unknown parameters in the instrumental variables regression in the presence of censored data under endogeneity.
Participation bias in the estimation of heritability and genetic correlation
arXiv:2405.19058v1 Announce Type: newAbstract: It is increasingly recognized that participation bias can pose problems for genetic studies.
Adaptive and Efficient Learning with Blockwise Missing and Semi-Supervised Data
arXiv:2405.18722v1 Announce Type: newAbstract: Data fusion is an important way to realize powerful and generalizable analyses across multiple sources.
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
arXiv:2305.15988v2 Announce Type: replace-crossAbstract: We study the problem of approximate sampling from non-log-concave distributions, e.
The Importance of Discussing Assumptions when Teaching Bootstrapping
arXiv:2112.07737v3 Announce Type: replace-crossAbstract: Bootstrapping and other resampling methods are increasingly appearing in the textbooks and curricula of courses that introduce undergraduate students to statistical methods.
nhppp: Simulating Nonhomogeneous Poisson Point Processes in R
arXiv:2402.00358v2 Announce Type: replaceAbstract: We introduce the `nhppp' package for simulating events from one-dimensional non-homogeneous Poisson point processes (NHPPPs) in R fast and with a small memory footprint.
Guided sequential ABC schemes for intractable Bayesian models
arXiv:2206.12235v5 Announce Type: replaceAbstract: Sequential algorithms such as sequential importance sampling (SIS) and sequential Monte Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a readily available likelihood function.
Bayesian Time-Varying Tensor Vector Autoregressive Models for Dynamic Effective Connectivity
arXiv:2106.14083v2 Announce Type: replace-crossAbstract: In contemporary neuroscience, a key area of interest is dynamic effective connectivity, which is crucial for understanding the dynamic interactions and causal relationships between different brain regions.
How to Simulate Realistic Survival Data? A Simulation Study to Compare Realistic Simulation Models
arXiv:2308.07842v2 Announce Type: replaceAbstract: In statistics, it is important to have realistic data sets available for a particular context to allow an appropriate and objective method comparison.
Causal inference in the closed-loop: marginal structural models for sequential excursion effects
arXiv:2405.18597v1 Announce Type: crossAbstract: Optogenetics is widely used to study the effects of neural circuit manipulation on behavior.