Latest Research
Unraveling stochastic fundamental diagrams considering empirical knowledge: modeling, limitation and further discussion
YuanzhengLei/Unraveling-stochastic-fundamental-diagrams-considering-empirical-knowledge
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The paper analyzes the influence of modeling empirical knowledge in the prior of the stochastic fundamental diagram model and whether empirical knowledge can improve the robustness and accuracy of the proposed model.
Applications
A unified generalization of inverse regression via adaptive column selection
A bottleneck of sufficient dimension reduction (SDR) in the modern era is that, among numerous methods, only the sliced inverse regression (SIR) is generally applicable under the high-dimensional settings.
Methodology
Multiple imputation for longitudinal data: A tutorial
Multiple imputation (MI) is widely used to handle missing data in such studies.
Methodology Applications
Bayesian Model Selection with Latent Group-Based Effects and Variances with the R Package slgf
We have selected data that reveal the shortcomings of classical analyses to emphasize the advantage our method can provide when a latent grouping structure is present.
Methodology
High-dimensional copula-based Wasserstein dependence
We generalize 2-Wasserstein dependence coefficients to measure dependence between a finite number of random vectors.
Methodology Statistics Theory Statistics Theory
Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood
The proposed method would give a useful guidance to address publication bias in meta-analysis of sparse data.
Methodology
Massively Parallel Computation of Similarity Matrices from Piecewise Constant Invariants
We present a computational framework for piecewise constant functions (PCFs) and use this for several types of computations that are useful in statistics, e. g., averages, similarity matrices, and so on.
Computation Mathematical Software Algebraic Topology 62-04 (Primary) 62R40 (Secondary) G.3; G.4
Supervised Contamination Detection, with Flow Cytometry Application
Here, we consider a supervised problem, where labeled samples drawn from both the reference distribution and the contamination distribution are available at training time.
Methodology
fastcpd: Fast Change Point Detection in R
Change point analysis is concerned with detecting and locating structure breaks in the underlying model of a sequence of observations ordered by time, space or other variables.
Methodology Computation
A Deep Learning Approach to Nonparametric Propensity Score Estimation with Optimized Covariate Balance
maosenpeng1/pslb-deep-learning
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This paper proposes a novel propensity score weighting analysis.
Methodology
