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 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 (MI) is widely used to handle missing data in such studies.
Methodology Applications
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
We generalize 2-Wasserstein dependence coefficients to measure dependence between a finite number of random vectors.
Methodology Statistics Theory Statistics Theory
The proposed method would give a useful guidance to address publication bias in meta-analysis of sparse data.
Methodology
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
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
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
This paper proposes a novel propensity score weighting analysis.
Methodology