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Found 15 papers, 3 papers with code
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines.
Limit theorems of Chatterjee's rank correlation
Establishing the limiting distribution of Chatterjee's rank correlation for a general, possibly non-independent, pair of random variables has been eagerly awaited to many.
Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance
The Gaussian-smoothed optimal transport (GOT) framework, pioneered in Goldfeld et al. (2020) and followed up by a series of subsequent papers, has quickly caught attention among researchers in statistics, machine learning, information theory, and related fields.
Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions.
Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians.
Robust Portfolio Optimization
We propose a robust portfolio optimization approach based on quantile statistics.
An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
Theoretically, we develop a theory for testing the equality of U-statistic based correlation matrices.
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA).
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
In this paper we focus on the principal component regression and its application to high dimension non-Gaussian data.
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions.
ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
In the non-sparse setting, we show that ECA's performance is highly related to the effective rank of the covariance matrix.
Challenges of Big Data Analysis
Big Data bring new opportunities to modern society and challenges to data scientists.
A Direct Estimation of High Dimensional Stationary Vector Autoregressions
In addition, we provide thorough experiments on both synthetic and real-world equity data to show that there are empirical advantages of our method over the lasso-type estimators in both parameter estimation and forecasting.
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension $d$ to scale with the series length $T$.
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution
The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix.
