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混合条件下多变量非参数模型的变化点检测与推断AI 解读

This paper studies multivariate nonparametric change point localization and inference problems. The data consists of a multivariate time series with potentially short range dependence. The distribution of this data is assumed to be piecewise constant with densities in a Hölder class. The change points, or times at which the distribution changes, are unknown. We derive the limiting distributions of the change point estimators when the minimal jump size vanishes or remains…

交互效应面板中的多重结构突变与量化宽松对银行放贷的影响AI 解读

This paper develops a new toolbox for multiple structural break detection in panel data models with interactive effects. The toolbox includes tests for the presence of structural breaks, a break date estimator, and a break date confidence interval. The new toolbox is applied to a large panel of US banks for a period characterized by massive quantitative easing programs aimed at lessening the impact of the global financial crisis and the COVID--19 pandemic. The question we…

隐半马尔可夫模型的序贯贝叶斯学习AI 解读

In this paper, we explore the class of the Hidden Semi-Markov Model (HSMM), a flexible extension of the popular Hidden Markov Model (HMM) that allows the underlying stochastic process to be a semi-Markov chain. HSMMs are typically used less frequently than their basic HMM counterpart due to the increased computational challenges when evaluating the likelihood function. Moreover, while both models are sequential in nature, parameter estimation is mainly conducted via batch…

时间序列的灵活条件密度估计AI 解读

This paper introduces FlexCodeTS, a new conditional density estimator for time series. FlexCodeTS is a flexible nonparametric conditional density estimator, which can be based on an arbitrary regression method. It is shown that FlexCodeTS inherits the rate of convergence of the chosen regression method. Hence, FlexCodeTS can adapt its convergence by employing the regression method that best fits the structure of data. From an empirical perspective, FlexCodeTS is compared to…

极端依赖与伴随次序统计量的关系AI 解读

The study of concomitants has recently met a renewed interest due to its applications in selection procedures. For instance, concomitants are used in ranked-set sampling, to achieve efficiency and reduce cost when compared to the simple random sampling. In parallel, the search for new methods to provide a rich description of extremal dependence among multiple time series has rapidly grown, due also to its numerous practical implications and the lack of suitable models to…

基于因子调整向量自回归的高维时间序列分段AI 解读

Vector autoregressive (VAR) models are popularly adopted for modelling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modelling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose…

通过 MCMC 实现经典估计的方法AI 解读

This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric…

无对照组两时点数据的重抽样因果推断及健康风险应用AI 解读

Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-$2$ diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called 'I-Rand' for independently sampling one of the two time points for each…

SUTSE 模型的快速同期预测及理论性质AI 解读

We consider the problem of forecasting multivariate time series by a Seemingly Unrelated Time Series Equations (SUTSE) model. The SUTSE model usually assumes that error variables are correlated. A crucial issue is that the model estimation requires heavy computational loads because of a large matrix computation, especially for high-dimensional data. To alleviate the computational issue, we propose a two-stage procedure for forecasting. First, we perform the Kalman filter as…

加性单指标协整时间序列模型的稳健 M 估计AI 解读

Robust M-estimation uses loss functions, such as least absolute deviation (LAD), quantile loss and Huber's loss, to construct its objective function, in order to for example eschew the impact of outliers, whereas the difficulty in analysing the resultant estimators rests on the nonsmoothness of these losses. Generalized functions have advantages over ordinary functions in several aspects, especially generalized functions possess derivatives of any order. Generalized…

局部单位根自回归中的系数随机性检验AI 解读

In this study, we propose a test for the coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given the results of earlier studies. Under this specification, we theoretically analyze the effect of the correlation between the random coefficient and disturbance on tests' properties, which remains largely unexplored in the literature. Our analysis reveals that the correlation crucially affects the…

类别型背景变量下的深度反事实估计AI 解读

Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the "What if ?" question retrospectively. The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process. However, such models are seldom available in practice and one usually wishes to infer them from observational data alone. Unfortunately, the correct structural equation model is…

平滑且可数据自适应的经验 Copula 重抽样方法AI 解读

We investigate the validity of two resampling techniques when carrying out inference on the underlying unknown copula using a recently proposed class of smooth, possibly data-adaptive nonparametric estimators that contains empirical Bernstein copulas (and thus the empirical beta copula). Following \cite{KirSegTsu21}, the first resampling technique is based on drawing samples from the smooth estimator and can only can be used in the case of independent observations. The…

用于分位数概率建模的神经样条搜索AI 解读

Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility. Modeling target distribution at arbitrary quantile levels and at arbitrary input attribute levels are important to offer a comprehensive picture of the data, and requires the quantile function to be expressive enough. The quantile function describing the target distribution using quantile levels is critical for quantile regression. Although various…

连续函数型数据的可分离性检验AI 解读

Analyzing the covariance structure of data is a fundamental task of statistics. While this task is simple for low-dimensional observations, it becomes challenging for more intricate objects, such as multivariate functions. Here, the covariance can be so complex that just saving a non-parametric estimate is impractical and structural assumptions are necessary to tame the model. One popular assumption for space-time data is separability of the covariance into purely spatial…

利用回声状态网络从稀疏时间序列中发现因果关系AI 解读

Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. However, reliable causal discovery can be very challenging, especially when the data acquisition rate varies (i.e., non-uniform data sampling), or in the presence of missing data points (e.g., sparse data sampling). To address these issues, we proposed a new system comprised of two parts, the first part fills missing data with a…

融合预测变量的多受试者动态功能连接贝叶斯方法AI 解读

Time Varying Functional Connectivity (TVFC) investigates how the interactions among brain regions vary over the course of an fMRI experiment. The transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort as measured by pupil dilation. In this paper, we develop a multi-subject Bayesian framework for estimating dynamic…

非平稳多元时间序列的频带分析AI 解读

Information from frequency bands in biomedical time series provides useful summaries of the observed signal. Many existing methods consider summaries of the time series obtained over a few well-known, pre-defined frequency bands of interest. However, these methods do not provide data-driven methods for identifying frequency bands that optimally summarize frequency-domain information in the time series. A new method to identify partition points in the frequency space of a…

异方差与结构突变时间序列的广义最小二乘 Granger 检验AI 解读

This paper proposes a novel method (GLS Granger test) to determine causal relationships between time series based on the estimation of the autocovariance matrix and generalized least squares. We show the effectiveness of proposed autocovariance matrix estimator (the sliding autocovariance matrix) and we compare the proposed method with the classical Granger F-test with via a synthetic dataset and a real dataset composed by cryptocurrencies. The simulations show that the…

基于分位数自回归的非因果性检验AI 解读

Non-causal processes have been drawing attention recently in Macroeconomics and Finance for their ability to display nonlinear behaviors such as asymmetric dynamics, clustering volatility, and local explosiveness. In this paper, we investigate the statistical properties of empirical conditional quantiles of non-causal processes. Specifically, we show that the quantile autoregression (QAR) estimates for non-causal processes do not remain constant across different quantiles in…

序列相关误差下删失回归的贝叶斯方法AI 解读

The problem of estimating censored linear regression models with autocorrelated errors arises in many environmental and social studies. The present work proposes a Bayesian approach to estimate censored regression models with AR(p) errors. The algorithm developed here considers the Gibbs sampler with data augmentation(GDA), in which, at each iteration, both the model parameters and the latent variables are sampled. The data augmentation is achieved from multiple sampling of…

使用 R 的 smooth 包进行预测AI 解读

There are many forecasting related packages in R with varied popularity, the most famous of all being \texttt{forecast}, which implements several important forecasting approaches, such as ARIMA, ETS, TBATS and others. However, the main issue with the existing functionality is the lack of flexibility for research purposes, when it comes to modifying the implemented models. The R package \texttt{smooth} introduces a new approach to univariate forecasting, implementing ETS and…

协变量引导的多元时间序列贝叶斯混合模型AI 解读

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate time series and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this paper, we propose a group-based method to cluster a collection of multivariate…

高频尾部风险测度：含单位根预测变量的 L1 正则化极值回归AI 解读

We study tail risk dynamics in high-frequency financial markets and their connection with trading activity and market uncertainty. We introduce a dynamic extreme value regression model accommodating both stationary and local unit-root predictors to appropriately capture the time-varying behaviour of the distribution of high-frequency extreme losses. To characterize trading activity and market uncertainty, we consider several volatility and liquidity predictors, and propose a…