Some theoretical properties of Silverman's method for Smoothed functional principal component analysis

Principal component analysis (PCA) is one of the key techniques in functional data analysis. One important feature of functional PCA is that there is a need for smoothing or regularizing of the estimated principal component curves. Silverman’s method for smoothed functional principal component analysis is an important approach in a situation where the sample curves are fully observed due to its theoretical and practical advantages. However, lack of knowledge about the theoretical properties of this method makes it difficult to generalize it to the situation where the sample curves are only observed at discrete time points. In this paper, we first establish the existence of the solutions of the successive optimization problems in this method. We then provide upper bounds for the bias parts of the estimation errors for both eigenvalues and eigenfunctions. We also prove functional central limit theorems for the variation parts of the estimation errors. As a corollary, we give the convergence rates of the estimations for eigenvalues and eigenfunctions, where these rates depend on both the sample size and the smoothing parameters. Under some conditions on the convergence rates of the smoothing parameters, we can prove the asymptotic normalities of the estimations.     

利率期限结构的B-样条校准法

瞬时远期利率曲线是利率期限结构的重要表现形式.本文介绍了如何应用B样条方法及序列二次规划算法,根据市场利率产品的报价,快速准确地拟合出远期利率曲线.不同于常用的Bootstrapping方法,我们的方法所产生的曲线满足利率期限结构所要求具有的光滑性.最后作为一个实际应用,本文使用欧元市场数据说明了我们方法的具体应用.     

Extremes of projections of functional time series on data–driven basis systems

The paper is concerned with the extreme behavior of projections of time series of functions onto data-driven basis systems, for example, on the estimated functional principal components. The coefficients of these projections, called the scores, encode the shapes of the curves. Within the framework of functional data analysis, the extreme shapes are those corresponding to multivariate extremes of the scores. The scores are not directly observable, and must be computed from the data. Even for iid Gaussian functions, they form a triangular array of dependent non–Gaussian vectors. Thus, even though the extreme behavior of the population scores of Gaussian functions follows from well–known results, it is not clear what the extreme behavior of their approximations computed from the data is. We clarify these issues for Gaussian functions and for more general functional time series whose projections are in the Gumbel domain of attraction.     

利率期限结构的主成分分析

本文采用主成分分析的方法对我国的利率期限结构进行了研究。在采用这种方法的同时,结合非线性变换BOX—COX得出了我国的利率期限结构具有代表性的三个主成分:利率期限结构曲线的平移、斜率的变化以及曲率的变化。同时通过实证分析证实了这种方法的有效性。     

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With the advance of computer storage capacity and online observation technique, more and more data are collected with curves and images. The most two important feature of curve and image data are high-dimension and high correlation between adjacent data. Functional data analysis has more advantage in deal with these data, which can not be treated by traditional multivariate statistics methods. Recently, a variety of functional data methods have been developed, including curve alignment, principal component analysis, regression, classification and clustering. In this paper, we mainly introduce the origins,development and recent process of functional data. Specifically, we firstly introduce the notion of functional data. Secondly, functional principal component analysis has been presented. Then, this paper is devoted to introduce estimation, variable selection and hypothesis testing of functional regression models. Lastly, the paper concludes with a brief discussion of future directions.     

On Shape Preserving C2 Hermite Interpolation

We propose a general parametric local approach for functional C ² Hermite shape preserving interpolation. The constructed interpolant is a parametric curve which interpolate values, first and second derivatives of a given function and reproduces the behavior of the data. The method is detailed for parametric curves with piecewise cubic components. For the selected space necessary and sufficient conditions are derived to ensure the convexity of the constructed interpolant. Monotonicity is also studied. The approximation order is investigated for both cases. The use of a parametric curves to interpolate data from a function can be considered a disadvantage of the scheme. However, the simple structure of the used curve greatly reduces such a disadvantage.     

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One key difference of analyzing functional data from multidimensional data is that one needs to take phase variation (described by warping functions) into consideration as well as amplitude variation. Nonparametric estimation of warping functions may not generate summary measures that are easily interpreted or compared. We propose a local nonlinear parametric model to capture major local variation including both phase variation and amplitude variation. The parameters are interpretable, and can be easily compared among different curves. Simulation and real data analysis are performed to illustrate the powerfulness of the method.     

Modeling and prediction of children’s growth data via functional principal component analysis

We use the functional principal component analysis (FPCA) to model and predict the weight growth in children. In particular, we examine how the approach can help discern growth patterns of underweight children relative to their normal counterparts, and whether a commonly used transformation to normality plays any constructive roles in a predictive model based on the FPCA. Our work supplements the conditional growth charts developed by Wei and He (2006) by constructing a predictive growth model based on a small number of principal components scores on individual’s past. This work was supported by National Natural Science Foundation of China (Grant No. 10828102), a Changjiang Visiting Professorship, the Training Fund of Northeast Normal University’s Scientific Innovation Project (Grant No. NENU-STC07002) and the National Institutes of Health Grant of USA (Grant No. R01GM080503-01A1).     

InfoVis Is So Much More: A Comment on Gelman and Unwin and an Invitation to Consider the Opportunities

We propose new tools for visualizing large amounts of functional data in the form of smooth curves. The proposed tools include functional versions of the bagplot and boxplot, which make use of the first two robust principal component scores, Tukey’s data depth and highest density regions.

By-products of our graphical displays are outlier detection methods for functional data. We compare these new outlier detection methods with existing methods for detecting outliers in functional data, and show that our methods are better able to identify outliers.

An R-package containing computer code and datasets is available in the online supplements.     

基于逐段曲线回归分析的息票剥离法的研究及应用

对一般息票剥离法(Bootstrap m ethod)进行了改进,将其在两个最近期间之间的利率利用线性关系进行估计,扩展为由回归分析逐段拟合曲线给予预测和估值.并给出了模型需要修正的情况和一个一般的修正方法.最后与N elson-S iegel模型构造收益率曲线的方法进行了对比实证分析,并给出了相应的分析结果.     

A Penalized Likelihood Approach to Rotation of Principal Components

A new paradigm for enhancing the interpretability of principal components through rotation is presented within the framework of penalized likelihood. The rotated components are computed as the maximizers of a Gaussian-based profile log-likelihood function plus a penalty term defined by a standard rotation criterion. This method enjoys a number of advantages over other methods for principal component rotation, notably (1) the rotation specifically targets ill-defined principal components, which may benefit the most from rotation, and (2) the connection with likelihood allows assessment of the fidelity of the rotated components to the data, thereby guiding the choice of penalty parameter. The method is illustrated with an application to a small functional dataset. Efficient computation of the penalized likelihood solution is possible using recently developed algorithms for optimization under orthogonality constraints.     

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This paper studies estimation in functional partial linear composite quantile regression model in which the dependent variable is related to both a function-valued random variable in linear form and a real-valued random variable in nonparametric form. The functional principal component analysis and regression splines are employed to estimate the slope function and the nonparametric function respectively, and the convergence rates of the estimators are obtained under some regularity conditions. Simulation studies and a real data example are presented for illustration of the performance of the proposed estimators.     

Simulation and evaluation of the distribution of interest rate risk

We study methods to simulate term structures in order to measure interest rate risk more accurately. We use principal component analysis of term structure innovations to identify risk factors and we model their univariate distribution using GARCH-models with Student’s t-distributions in order to handle heteroscedasticity and fat tails. We find that the Student’s t-copula is most suitable to model co-dependence of these univariate risk factors. We aim to develop a model that provides low ex-ante risk measures, while having accurate representations of the ex-post realized risk. By utilizing a more accurate term structure estimation method, our proposed model is less sensitive to measurement noise compared to traditional models. We perform an out-of-sample test for the U.S. market between 2002 and 2017 by valuing a portfolio consisting of interest rate derivatives. We find that ex-ante Value at Risk measurements can be substantially reduced for all confidence levels above 95%, compared to the traditional models. We find that that the realized portfolio tail losses accurately conform to the ex-ante measurement for daily returns, while traditional methods overestimate, or in some cases even underestimate the risk ex-post. Due to noise inherent in the term structure measurements, we find that all models overestimate the risk for 10-day and quarterly returns, but that our proposed model provides the by far lowest Value at Risk measures.

     

Term structure of discount rates for firms in the insurance industry

There is strong evidence in the literature for the hypothesis that interest rates and the market risk premium are not constant during the business cycle. The beta risk of firms in the insurance industry is also time-varying. The major implication of these results is that discount rates for risky cash flows are time varying and must obey a term structure similar to the term structure of interest rates. The purpose of this paper is to estimate discount rates for cash flows with different time horizons for the U.S. insurance industry and for different insurance sectors. We find that the term structure cost of capital takes on different shapes depending on the business cycle. It is therefore meaningful for insurers to evaluate risky projects by selecting a discount rate most appropriate for the nature and the time horizon of each project.     

我国上市公司资本结构影响因素实证分析

本文选取了可能影响企业资本结构的多个指标变量进行分析 ,利用主成分分析提供的方法将变量综合成彼此互不相关的少数几个主成分。再用主成分 (作为回归自变量 )对企业的 5种资本负债比 (作为回归因变量 )进行多元回归分析 ,得出了影响企业资本结构的主要因素 ,以及这些因素与企业资本结构之间的关系 ,为企业确定资本结构提供参考依据 ,为企业财务决策提供支持。     

基于双因素利率期限结构模型的国债市场利率行为研究

本引用一种新的计量经济学方法-高斯估计法，通过Gauss语言编程，使用国债市场短期利率数据对双因素连续时间利率期限结构模型进行了参数估计和预测，得出的结果较理想，从而能更好的了解国债市场短期利率行为特点。     

Functional outlier detection with robust functional principal component analysis

Functional principal component analysis is the preliminary step to represent the data in a lower dimensional space and to capture the main modes of variability of the data by means of small number of components which are linear combinations of original variables. Sensitivity of the variance and the covariance functions to irregular observations make this method vulnerable to outliers and may not capture the variation of the regular observations. In this study, we propose a robust functional principal component analysis to find the linear combinations of the original variables that contain most of the information, even if there are outliers and to flag functional outliers. We demonstrate the performance of the proposed method on an extensive simulation study and two datasets from chemometrics and environment.     

主成分分析法在高校学生质量综合评价中的应用

主成分分析法能够在保证原始数据信息损失最小的情况下,以少数的综合变量取代原有的多维变量,使数据结构大为简化,并且客观地确定变量权重,避免了主观随意性.应用主成分分析方法对高等学校学生质量进行了综合评价,根据综合得分给出了科学的排名,客观地反映了学生各方面的特征.     

Functional Data Model for Genetically Related Individuals With Application to Cow Growth

We propose a new version of functional data model for analyzing familial related individuals, where the within-subject correlation depends smoothly on a covariate such as age and the between-subject correlation follows family-wise genetic association. Our motivating example concerns measurements of weight as a function of age in sibling cows from independent families. Observations are sparsely sampled from trajectories of a phenotype contaminated with measurement error, where the phenotypic trajectory consists of a genetic component and an environmental component. By combining information across individuals, the genetic and environmental covariances are estimated via smoothing techniques. We study the genetic and environmental effects using principal component analysis, taking into account the genetic correlation to enhance the subject-level signal extraction. We show via the real data and simulations that incorporating the correlation structure improves predictions of individual phenotypic trajectories.     

Empirical analysis and forecasting of multiple yield curves

In this paper we perform a thorough empirical study of tenor-dependent term structures which reveals important cross-tenor dependencies of yields as a persistent feature of post-crisis interest rate markets. Based on this analysis, we develop tractable dynamic factor models to forecast multiple yield curves. We show that our method outperforms existing single-curve forecasting methods by taking into account the connections between rates of different tenor structures. Our results have important implications e.g. for risk management in finance and insurance as the disregard of tenor dependencies may lead to an underestimation of risks.