Wavelet Estimation for Factor Models with Time-Varying Loadings

Intrinsic wavelet regression for surfaces of Hermitian positive definite matrices

This paper develops intrinsic wavelet denoising methods for surfaces of Hermitian positive definite matrices, with in mind the application to nonparametric estimation of the time-varying spectral matrix of a multivariate locally stationary time series. First, we construct intrinsic average-interpolating wavelet transforms acting directly on surfaces of Hermitian positive definite matrices in a curved Riemannian manifold with respect to an affine-invariant metric. Second, we derive the wavelet coefficient decay and linear wavelet thresholding convergence rates of intrinsically smooth surfaces o…

针对高维混杂因素的非平稳信号的部分小波典型相干性

Partial Wavelet Canonical Coherence for Nonstationary Signals with High Dimensional Confounders

提出部分小波典型相干性，用于在调整高维混杂因素后测量两个多变量非平稳时间序列之间的直接典型关联，通过小波方法实现尺度特定、时变的关联度量。

Generalized Morse Wavelets as a Superfamily of Analytic Wavelets

The generalized Morse wavelets are shown to constitute a superfamily that essentially encompasses all other commonly used analytic wavelets, subsuming eight apparently distinct types of analysis filters into a single common form. This superfamily of analytic wavelets provides a framework for systematically investigating wavelet suitability for various applications. In addition to a parameter controlling the time-domain duration or Fourier-domain bandwidth, the wavelet {\em shape} with fixed bandwidth may be modified by varying a second parameter, called $γ$. For integer values of $γ$, the most…

非平稳因子模型的自适应估计及静态因子载荷检验

Adaptive Estimation for Non-stationary Factor Models And A Test for Static Factor Loadings

本文针对具有演化动态的局部平稳因子模型，提出自适应筛估计量估计时变载荷列空间和因子过程，并基于特征分析和 bootstrap 检验静态载荷假设，适用于高维情形。

A note on state space representations of locally stationary wavelet time series

In this note we show that the locally stationary wavelet process can be decomposed into a sum of signals, each of which following a moving average process with time-varying parameters. We then show that such moving average processes are equivalent to state space models with stochastic design components. Using a simple simulation step, we propose a heuristic method of estimating the above state space models and then we apply the methodology to foreign exchange rates data.

Sparse Bayesian time-varying covariance estimation in many dimensions

We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global-local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500 members. Our approach yields precise correlation estimates, strong implied minimum variance portfolio performance and superior…

小波典范相干性用于非平稳信号

Wavelet Canonical Coherence for Nonstationary Signals

WaveCanCoh通过小波分析方法，用于研究非平稳信号中不同集群间的时变依赖关系，能有效捕捉瞬时频率特定的相互作用。

贝叶斯协变量变化交互分析用于多元计数数据：微生物组研究的应用

Bayesian Covariate-Varying Interaction Analysis for Multivariate Count Data: Application to Microbiome Studies

本文提出了一种贝叶斯协变量变化因子模型，用于分析微生物组数据中的多元计数数据，通过非参数贝叶斯框架解决高维和离散性等挑战。

时变矩阵因子模型

Time-Varying Matrix Factor Models

本文提出时变矩阵因子模型，利用非参数主成分分析刻画因子载荷的动态变化，并引入新方法解决旋转不确定性，应用于国际贸易网络分析。

Functional mixed effects wavelet estimation for spectra of replicated time series

Motivated by spectral analysis of replicated brain signal time series, we propose a functional mixed effects approach to model replicate-specific spectral densities as random curves varying about a deterministic population-mean spectrum. In contrast to existing work, we do not assume the replicate-specific spectral curves to be independent, i.e. there may exist explicit correlation between different replicates in the population. By projecting the replicate-specific curves onto an orthonormal wavelet basis, estimation and prediction is carried out under an equivalent linear mixed effects model …

具有时变主效应的矩阵值因子模型

Matrix-valued Factor Model with Time-varying Main Effects

本文提出矩阵值时变主效应因子模型（MEFM），推广传统矩阵值因子模型，提出各成分估计量并证明收敛性，通过检验与模拟验证了MEFM的必要性。

存在混杂时合成控制法的识别与推断

Identification and Inference for Synthetic Controls with Confounding

本文在存在未观测混杂的面板数据中，利用随机因子模型研究处理效应的识别与推断，揭示时间与个体层面混杂的权衡，并为多种合成控制估计量建立渐近推断结果。

网络上的多尺度动态依赖性估计

Multiscale Dynamic Dependence Estimation over Networks

提出网络局部平稳小波过程（Net-LSW），将图结构直接编码在协方差中，并引入局部偏相关图，开发子过程估计方案，准确恢复非平稳依赖结构。

在具有时间依赖观测的高维条件时间变化因子模型中测试Alpha

Testing Alpha in High-Dimensional Conditional Time-Varying Factor Models with Dependent Observations

本文研究了在高维条件时间变化因子模型中具有时间依赖观测的Alpha测试问题，提出了一种基于B样条筛方法的求和检验、最大检验以及适应性推断的Cauchy组合检验，理论推导了估计的平均Alpha的随机展开，并证明了求和统计量的渐近正态性，以及最大统计量的极值极限理论。

Analysis of Modulated Multivariate Oscillations

The concept of a common modulated oscillation spanning multiple time series is formalized, a method for the recovery of such a signal from potentially noisy observations is proposed, and the time-varying bias properties of the recovery method are derived. The method, an extension of wavelet ridge analysis to the multivariate case, identifies the common oscillation by seeking, at each point in time, a frequency for which a bandpassed version of the signal obtains a local maximum in power. The lowest-order bias is shown to involve a quantity, termed the instantaneous curvature, which measures th…