Hazard models with varying coefficients for multivariate failure time data

Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency and asymptotic normality of the proposed estimators are established and standard error formulas for the estimated coefficients are derived and empirically tested. To reduce the computational burden of the maximum local pseudo-partial likelihood estimator, a simple and useful one-step estimator is proposed. Statistical properties of the one-step estimator are established and simulation studies are conducted to compare the performance of the one-step estimator to that of the maximum local pseudo-partial likelihood estimator. The results show that the one-step estimator can save computational cost without compromising performance both asymptotically and empirically and that an optimal weighted average estimator is more efficient than the maximum local pseudo-partial likelihood estimator. A data set from the Busselton Population Health Surveys is analyzed to illustrate our proposed methodology.Comment: Published at http://dx.doi.org/10.1214/009053606000001145 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Distributions of Angles in Random Packing on Spheres

This paper studies the asymptotic behaviors of the pairwise angles among n randomly and uniformly distributed unit vectors in Rp as the number of points n → ∞, while the dimension p is either fixed or growing with n. For both settings, we derive the limiting empirical distribution of the random angles and the limiting distributions of the extreme angles. The results reveal interesting differences in the two settings and provide a precise characterization of the folklore that “all high-dimensional random vectors are almost always nearly orthogonal to each other”. Applications to statistics and machine learning and connections with some open problems in physics and mathematics are also discussed

Distributions of Angles in Random Packing on Spheres

This paper studies the asymptotic behaviors of the pairwise angles among n randomly and uniformly distributed unit vectors in Rp as the number of points n → ∞, while the dimension p is either fixed or growing with n. For both settings, we derive the limiting empirical distribution of the random angles and the limiting distributions of the extreme angles. The results reveal interesting differences in the two settings and provide a precise characterization of the folklore that “all high-dimensional random vectors are almost always nearly orthogonal to each other”. Applications to statistics and machine learning and connections with some open problems in physics and mathematics are also discussed

Efficient Premature Ventricular Contraction Detection Based on Network Dynamics Features

Automatic detection of premature ventricular contractions (PVCs) is essential for early identification of cardiovascular abnormalities and reduction of clinical workload. As the most prevalent arrhythmia, PVCs can cause cardiac failure or sudden death. The difficulty resides in extracting features that effectively reflect the electrocardiogram (ECG) signals. Transition networks (TN), which represent the transition relationships between various phases of a time series, are advantageous for capturing temporal dynamics. Therefore, in order to recognize PVCs, each heartbeat was firstly split into serval segments; then their statistical properties were calculated for the sequence construction; finally, network topology related features were extracted from TN constructed by these sequences of statistical properties, and input into decision trees-based Gentleboost for PVC recognition. The algorithm was trained on MIT-BIH arrhythmia database (MIT-BIH-AR), and tested on St. Petersburg Institute of Cardiological Technics 12-lead arrhythmia database (INCART), wearable ECG database (WECG), and noise stress test database by four evaluation metrics: sensitivity, positive predictivity, F1-score (F1) and area under the curve (AUC). The proposed algorithm achieved an average F1 of 0.9784 and AUC of 0.9975 on MIT-BIH-AR, and proved good generalization ability on INCART and WECG with F1=0.9633 and 0.9467, AUC=0.9887 and 0.9755, respectively. The algorithm also exhibited robustness and noise immunity as evidenced by tests on sensitivity of R-wave peak offset and noise, and real-world daily life conditions. Overall, the proposed PVC detection algorithm based on TN theory offered high classification accuracy, strong robustness, and good generalization ability, with great potential for wearable mobile applications

Gaining efficiency via weighted estimators for multivariate failure time data

Multivariate failure time data arise frequently in survival analysis. A commonly used technique is the working independence estimator for marginal hazard models. Two natural questions are how to improve the efficiency of the working independence estimator and how to identify the situations under which such an estimator has high statistical efficiency. In this paper, three weighted estimators are proposed based on three different optimal criteria in terms of the asymptotic covariance of weighted estimators. Simplified close-form solutions are found, which always outperform the working independence estimator. We also prove that the working independence estimator has high statistical efficiency, when asymptotic covariance of derivatives of partial log-likelihood functions is nearly exchangeable or diagonal. Simulations are conducted to compare the performance of the weighted estimator and working independence estimator. A data set from Busselton population health surveys is analyzed using the proposed estimators

Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.Comment: 10 page

A Label-Free Quantitative Proteomic Analysis of Mouse Neutrophil Extracellular Trap Formation Induced by Streptococcus suis or Phorbol Myristate Acetate (PMA)

Streptococcus suis (S. suis) ranks among the five most important porcine pathogens worldwide and occasionally threatens human health, particularly in people who come into close contact with pigs or pork products. An S. suis infection induces the formation of neutrophil extracellular traps (NETs) in vitro and in vivo, and the NET structure plays an essential role in S. suis clearance. However, the signaling pathway by which S. suis induces NET formation remains to be elucidated. In the present study, we used a label-free quantitative proteomic analysis of mouse NET formation induced by S. suis or phorbol myristate acetate (PMA), a robust NET inducer. Greater than 50% of the differentially expressed proteins in neutrophils infected by S. suis showed similar changes as observed following PMA stimulation, and PKC, NADPH oxidase, and MPO were required for NET formation induced by both stimuli. Because PMA induced robust NET formation while S. suis (MOI = 2) induced only weak NET formation, the association between the inducer and NET formation was worth considering. Interestingly, proteins involved in peptidase activity showed significant differential changes in response to each inducer. Of these peptidases, MMP-8 expression was obviously decreased in response to PMA, but it was not significantly changed in response to S. suis. A subsequent study further confirmed that MMP-8 activity was inversely correlated with NET formation induced by both stimuli. Therefore, the present study provides potentially important information about the manner by which neutrophils responded to the inducers to form NETs