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具有交互固定效应的面板数据模型AI 解读

This paper considers large N and large T panel data models with unobservable multiple interactive effects. These models are useful for both micro and macro econometric modelings. In earnings studies, for example, workers ’ motivation, persistence, and diligence combined to influence the earnings in addition to the usual argument of innate ability. In macroeconomics, the interactive effects represent unobservable common shocks and their heterogeneous responses over cross…

高维稀疏因子建模：基因表达组学应用AI 解读

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical…

重复测量分析中非参数变系数模型的变量选择AI 解读

Nonparametric varying-coefficient models are commonly used for analysis of data measured repeatedly over time, including longitudinal and functional responses data. While many procedures have been developed for estimating the varying-coefficients, the problem of variable selection for such models has not been addressed. In this article, we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped…

结构性无应答下多层调查数据的域级协方差分析AI 解读

Health care quality surveys in the United States are administered to individual respondents (i.e., hospital patients, health plan members) to evaluate the performance of health care organizations (i.e., hospitals, health plans), which thus constitute estimation domains. For better understanding and more parsimonious reporting of dimensions of quality, we analyze relationships among quality measures at the domain level. Rather than specifying a full parametric model for the…

函数型加性模型AI 解读

In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means that the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption and propose to replace it by an additive structure, leading to a more widely applicable and much more flexible framework for functional regression models. The proposed…

主成分分析的有限样本近似：矩阵扰动方法AI 解读

Principal component analysis (PCA) is a standard tool for dimensional reduction of a set of n observations (samples), each with p variables. In this paper, using a matrix perturbation approach, we study the nonasymptotic relation between the eigenvalues and eigenvectors of PCA computed on a finite sample of size n, and those of the limiting population PCA as n→∞. As in machine learning, we present a finite sample theorem which holds with high probability for the closeness…

利用随机矩阵理论估计高维协方差矩阵的谱AI 解读

Estimating the eigenvalues of a population covariance matrix from a sample covariance matrix is a problem of fundamental importance in multivariate statistics; the eigenvalues of covariance matrices play a key role in many widely used techniques, in particular in principal component analysis (PCA). In many modern data analysis problems, statisticians are faced with large datasets where the sample size, n, is of the same order of magnitude as the number of variables p. Random…

金融与风险管理中的计量建模：概览AI 解读

This paper gives an overview about the sixteen papers included in this special issue. The papers in this special issue cover a wide range of topics. Such topics include discussing a class of tests for correlation, estimation of realized volatility, modeling time series and continuous-time models with long-range dependence, estimation and specification testing of time series models, estimation in a factor model with high-dimensional problems, finite-sample examination of…

使用因子模型估计高维协方差矩阵AI 解读

High dimensionality comparable to sample size is common in many statistical problems. We examine covariance matrix estimation in the asymptotic framework that the dimensionality p tends to [infinity] as the sample size n increases. Motivated by the Arbitrage Pricing Theory in finance, a multi-factor model is employed to reduce dimensionality and to estimate the covariance matrix. The factors are observable and the number of factors K is allowed to grow with p. We investigate…

全球收益率曲线动态与互动：动态 Nelson–Siegel 方法AI 解读

The popular Nelson-Siegel [Nelson, C.R., Siegel, A.F., 1987. Parsimonious modeling of yield curves. Journal of Business 60, 473-489] yield curve is routinely fit to cross sections of intra-country bond yields, and Diebold-Li [Diebold, F.X., Li, C., 2006. Forecasting the term structure of government bond yields. Journal of Econometrics 130, 337-364] have recently proposed a dynamized version. In this paper we extend Diebold-Li to a global context, modeling a potentially large…

大量预测变量下的预测：贝叶斯收缩能否替代主成分？AI 解读

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study conditions for consistency of the forecast based on Bayesian regression as the cross-section and the sample size become large. This analysis…

使用定向预测变量预测经济时间序列AI 解读

This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be non-linear. Second, the factors used in the forecasting equation are estimated in a way to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to 'targeted predictors' selected using…

用于估计与分解违约风险的非高斯面板时间序列模型AI 解读

We model 1981–2005 quarterly default frequencies for a panel of U.S. firms in different rating and age classes from the Standard and Poor database. The data are decomposed into systematic and firm-specific risk components, where the systematic component reflects the general economic conditions and the default climate. We need to cope with: the shared exposure of each age cohort, industry, and rating class to the same systematic risk factor; strongly non-Gaussian features of…

随机波动模型中的分数协整AI 解读

Asset returns are frequently assumed to be determined by one or more common factors. We consider a bivariate factor model where the unobservable common factor and idiosyncratic errors are stationary and serially uncorrelated but have strong dependence in higher moments. Stochastic volatility models for the latent variables are employed, in view of their direct application to asset pricing models. Assuming that the underlying persistence is higher in the factor than in the…

利用主成分联合建模成对稀疏函数数据AI 解读

We propose a modelling framework to study the relationship between two paired longitudinally observed variables. The data for each variable are viewed as smooth curves measured at discrete time-points plus random errors. While the curves for each variable are summarized using a few important principal components, the association of the two longitudinal variables is modelled through the association of the principal component scores. We use penalized splines to model the mean…

利用中位数与球面主成分进行稳健函数估计AI 解读

We present robust estimators for the mean and the principal components of a stochastic process in . Robustness and asymptotic properties of the estimators are studied theoretically, by simulation and by example. It is shown that the proposed estimators are generally more robust to outliers than the commonly used sample mean and principal components, although their properties depend on the spacings of the eigenvalues of the covariance function. Copyright 2008, Oxford…

用潜在高斯过程建模稀疏广义纵向观测AI 解读

Summary In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process model for such data, establishing a connection to functional data analysis. The functional methods proposed are…

数据丰富环境下收益率曲线预测：无套利因子增强 VAR 方法AI 解读

This paper suggests a term structure model which parsimoniously exploits a broad macroeconomic information set. The model uses the short rate and the common components of a large number of macroeconomic variables as factors. Precisely, the dynamics of the short rate are modeled with a Factor-Augmented Vector Autoregression and the term structure is derived using parameter restrictions implied by no-arbitrage. The model has economic appeal and provides better out-of-sample…

大型条件异方差因子模型的间接估计：道琼斯30只股票应用AI 解读

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small…

乳腺密度数据缺失时病例对照与队列设计中的乳腺癌相对风险估计AI 解读

We analyzed data from the Breast Cancer Detection Demonstration Project (BCDDP) to obtain multivariate relative hazard models for breast cancer that included mammographic density (MD) in addition to standard risk factors. Data from the BCDDP were collected from a stratified case–control study in the screening phase (1973–1980) and from follow-up of three subcohorts in the follow-up phase (1980–1995). For both phases, MD measurements were only available for about half the…

高维小样本数据中聚类的统计显著性AI 解读

Clustering methods provide a powerful tool for the exploratory analysis of high-dimension, low–sample size (HDLSS) data sets, such as gene expression microarray data. A fundamental statistical issue in clustering is which clusters are “really there,” as opposed to being artifacts of the natural sampling variation. We propose SigClust as a simple and natural approach to this fundamental statistical problem. In particular, we define a cluster as data coming from a single…

基于广义双曲分布的非参数风险管理AI 解读

In this article we propose the generalized hyperbolic adaptive volatility (GHADA) risk management model based on the generalized hyperbolic (GH) distribution and on a nonparametric adaptive methodology. Compared with the normal distribution, the GH distribution has semiheavy tails and represents the financial risk factors more appropriately. Nonparametric adaptive methodology has the desirable property of being able to estimate homogeneous volatility over a short time…

高维变量选择与回归中的“预处理”AI 解读

We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function, yielding a “preconditioned” response variable. The primary method used for this initial regression is supervised principal components. Then we apply a standard procedure such as forward stepwise selection or the LASSO to the preconditioned response variable. In a number of simulated…

模仿组合、经济风险溢价与多贝塔模型检验AI 解读

We consider two formulations of the linear factor model (LFM) with nontraded factors. In the first formulation, LFM, risk premia and alphas are estimated by a cross-sectional regression of average returns on betas. In the second formulation, LFM*, the factors are replaced by their projections on the span of excess returns, and risk premia and alphas are estimated by time series regressions. We compare the two formulations and study the small-sample properties of estimates…