Correlation and the time interval in multiple regression models

In this paper we investigate the time interval effect of multiple regression models in which some of the variables are additive and some are multiplicative. The effect on the partial regression and correlation coefficients is influenced by the selected time interval. We find that the partial regression and correlation coefficients between two additive variables approach one-period values as n increases. When one of the variables is multiplicative, they will approach zero in the limit. We also show that the decreasing speed of the n-period correlation coefficients between both multiplicative variables is faster than others, except that a one-period correlation has a higher positive value. The results of this paper can be widely applied in various fields where regression or correlation analyses are employed.     

Jackknife evaluation of uncertainty judgments aggregated by the Kullback-Leibler distance

One feasible approach to aggregating uncertainty judgments in risk assessments is to use calibration variables (or seed questions) and the Kullback-Leibler (K-L) distance to evaluate experts’ substantive or normative expertise and assign weights based on the corresponding scores. However, the reliability of this aggregation model and the effects of the number of seed questions or experts on the stability of the aggregated results are still at issue. To assess the stability of the aggregation model, this study applies the jackknife re-sampling technique to a large data set of real-world expert opinions. We also use a nonlinear regression model to analyze and interpret the resulting jackknife estimates. Our statistical model indicates that the stability of Cooke’s classical model, in which the components of the scoring rule are determined by the K-L distance, increases exponentially as the number of seed questions increases. Considering the difficulty and importance of creating and choosing appropriate seed variables, the results of this study justify the use of the K-L distance to determine and aggregate better probability interval or distribution estimates.     

A Note on an Input Control Model of a Predictive Regression Interval

A well-known problem of prediction in linear regression models is to find a confidence interval for the random value of the dependent variable when the values of the independent variables are given. Such a situation may arise in economic quality control models when the independent variables are costly inputs and the dependent variable is some measure of quality or production. In such a circumstance, an important control objective may be to find values for the inputs that will maximize the lower limit of the prediction confidence interval for a fixed budget, or alternatively, to minimize the cost of the inputs for a fixed lower limit of the confidence interval. In this paper, we shall show that global optima can be found using known algorithms. The special case of simple linear regression is discussed and an illustrative example is provided.     

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在线性回归模型建模中, 回归自变量选择是一个受到广泛关注、文献众多, 具有很强的理论和实际意义的问题. 回归自变量选择子集的相合性是其中一个重要问题, 如果某种自变量选择方法选择的子集在样本量趋于无穷时是相合的, 而且预测均方误差较小, 则这种方法是可取的. 利用BIC准则可以挑选相合的自变量子集, 但是在自变量个数很多时计算量过大; 适应lasso方法具有较高计算效率, 也能找到相合的自变量子集; 本文提出一种更简单的自变量选择方法, 只需要计算两次普通线性回归: 第一次进行全集回归, 得到全集的回归系数估计, 然后利用这些回归系数估计挑选子集, 然后只要在挑选的自变量子集上再进行一次普通线性回归就得到了回归结果. 考虑如下的回归模型: 其中回归系数中非零分量下标的集合为, 设是本文方法选择的自变量子集下标集合, 是本文方法估计的回归系数(未选中的自变量对应的系数为零), 本文证明了, 在适当条件下, 其中表示的 分量下标在中的元素的组成的向量, 是误差方差, 是与 矩阵极限有关的矩阵和常数. 数值模拟结果表明本文方法具有很好的中小样本性质.     

A multivariate nonparametric test of independence

A new nonparametric approach to the problem of testing the joint independence of two or more random vectors in arbitrary dimension is developed based on a measure of association determined by interpoint distances. The population independence coefficient takes values between 0 and 1, and equals zero if and only if the vectors are independent. We show that the corresponding statistic has a finite limit distribution if and only if the two random vectors are independent; thus we have a consistent test for independence. The coefficient is an increasing function of the absolute value of product moment correlation in the bivariate normal case, and coincides with the absolute value of correlation in the Bernoulli case. A simple modification of the statistic is affine invariant. The independence coefficient and the proposed statistic both have a natural extension to testing the independence of several random vectors. Empirical performance of the test is illustrated via a comparative Monte Carlo study.     

Low Mach number limit of viscous polytropic fluid flows

This paper studies the singular limit of the non-isentropic Navier-Stokes equations with zero thermal coefficient in a two-dimensional bounded domain as the Mach number goes to zero. A uniform existence result is obtained in a time interval independent of the Mach number, provided that the initial data satisfy the “bounded derivative conditions”, that is, the time derivatives up to order two are bounded initially, and Navier?s slip boundary condition is imposed.     

Efficient estimation for additive hazards regression with bivariate current status data

This paper discusses efficient estimation for the additive hazards regression model when only bi- variate current status data are available.Current status data occur in many fields including demographical studies and tumorigenicity experiments (Keiding,1991;Sun,2006) and several approaches have been proposed for the additive hazards model with univariate current status data (Lin et al.,1998;Martinussen and Scheike,2002).For bivariate data,in addition to facing the same problems as those with univariate data,one needs to deal with the association or correlation between two related failure time variables of interest.For this,we employ the copula model and an efficient estimation procedure is developed for inference.Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations.An illustrative example is provided.     

Assessing Local Influence in Canonical Correlation Analysis

The first order local influence approach is adopted in this paper to assess the local influence of observations to canonical correlation coefficients, canonical vectors and several relevant test statistics in canonical correlation analysis. This approach can detect different aspects of influence due to different perturbation schemes. In this paper, we consider two different kinds, namely, the additive perturbation scheme and the case-weights perturbation scheme. It is found that, under the additive perturbation scheme, the influence analysis of any canonical correlation coefficient can be simplified to just observing two predicted residuals. To do the influence analysis for canonical vectors, a scale invariant norm is proposed. Furthermore, by choosing proper perturbation scales on different variables, we can compare the different influential effects of perturbations on different variables under the additive perturbation scheme. An example is presented to illustrate the effectiveness of the first order local influence approach.     

Convergence of the compressible magnetohydrodynamic equations to incompressible magnetohydrodynamic equations

In this paper, we study the incompressible limit of the three-dimensional compressible magnetohydrodynamic equations, which models the dynamics of compressible quasi-neutrally ionized fluids under the influence of electromagnetic fields. Based on the convergence-stability principle, we show that, when the Mach number, the shear viscosity coefficient, and the magnetic diffusion coefficient are sufficiently small, the initial-value problem of the model has a unique smooth solution in the time interval where the ideal incompressible magnetohydrodynamic equations have a smooth solution. When the latter has a global smooth solution, the maximal existence time for the former tends to infinity as the Mach number, the shear viscosity coefficient, and the magnetic diffusion coefficient go to zero. Moreover, we obtain the convergence of smooth solutions for the model forwards those for the ideal incompressible magnetohydrodynamic equations with a sharp convergence rate.     

New power limits for extremes

For order statistics there is a deceptively simple link between affine and power norming, using exponential transforms. This link does not tell the whole story about limit distributions. The exponential transforms $W=e^{V}$ and $W=-e^{-V}$ yield limit variables which are either positive or negative. Under power norming there exist discrete limit distributions for maxima. The corresponding limit variables assume two values, one of which is zero. All variables with two values, one positive, one zero, are power limits for maxima. They are of different power type if they give different weight to zero, but they all have the same domain, the set of dfs with finite positive upper endpoint and an upper tail which varies slowly. So we see that convergence of types does not hold for power norming. This paper gives a classification of the power limits and their domains for maxima, variables conditioned to be large, and POTs (where power limits may assume three values). Convergence of sample clouds under power norming is studied, and of intermediate upper order statistics. The new power limits do not affect applications. Power norming is a viable alternative to classic extreme value theory. The extra norming constant in the exponent automatically improves the rate of convergence. Hill plots are a good instrument to determine this norming constant. It will be shown how to eliminate the bias of Hill plots and estimate high upper quantiles when the tail does not vary regularly or when convergence is slow.     

Multivariate regression estimates for finite populations

The theory on regression estimate based on one auxiliary variable has been extended to that for more than one auxiliary variable. It has been found that the multivariate regression estimate (MRE) is not unbiased in general. The form of the approximate standard error of MRE is the same as that of simple regression estimate based on one auxiliary variable with the exception that the multiple correlation coefficient replaces the total correlation coefficient in the expression. It has also been found that the precision of MRE is non-decreasing, rather usually increasing as the number of auxiliary variables correlated with the dependent variable increases, assuming sample size to be large compared to the number of auxiliary variables.     

一种基于三参数区间灰色语言变量的多属性群决策方法

针对属性值为三参数区间灰色语言变量的不确定型多属性决策问题进行了研究,本文将语言变量和三参数区间数融合, 提出了三参数区间灰色语言变量的概念, 定义了三参数区间灰色语言变量的运算规则和可能度公式,在此基础上建立了基于投影模型的三参数区间灰色语言变量的多属性群决策方法。最后,通过对移动银行服务质量评估案例验证本模型的有效性。     

Using Spatial Statistics to Select Model Complexity

Testing for nonindependence among the residuals from a regression or time series model is a common approach to evaluating the adequacy of a fitted model. This idea underlies the familiar Durbin–Watson statistic, and previous works illustrate how the spatial autocorrelation among residuals can be used to test a candidate linear model. We propose here that a version of Moran's I statistic for spatial autocorrelation, applied to residuals from a fitted model, is a practical general tool for selecting model complexity under the assumption of iid additive errors. The “space” is defined by the independent variables, and the presence of significant spatial autocorrelation in residuals is evidence that a more complex model is needed to capture all of the structure in the data. An advantage of this approach is its generality, which results from the fact that no properties of the fitted model are used other than consistency. The problem of smoothing parameter selection in nonparametric regression is used to illustrate the performance of model selection based on residual spatial autocorrelation (RSA). In simulation trials comparing RSA with established selection criteria based on minimizing mean square prediction error, smooths selected by RSA exhibit fewer spurious features such as minima and maxima. In some cases, at higher noise levels, RSA smooths achieved a lower average mean square error than smooths selected by GCV. We also briefly describe a possible modification of the method for non-iid errors having short-range correlations, for example, time-series errors or spatial data. Some other potential applications are suggested, including variable selection in regression models.     

论带有趋势变化的变量的相关:数值试验

当计算相关的二个变量都包含有明显的趋势变化成分时,原变量之间的相关特征可能被歪曲(夸大或者缩小).对此问题进行了数值试验,结果表明,变量带有性质相反的趋势变化,会使这二个变量之间的相关系数减小(正相关的数值减小,负相关被夸大).变量带有性质相同的趋势变化,会使这二个变量之间的相关系数增加(正相关被夸大,负相关数值变小).数值试验还表明,趋势变化对相关的影响具有可交换性.只要不改变它们趋势变化的数值,它们叠加的变量互相交换,影响相关系数的后果是一样的;研究还指出,二个变量有相同的变化趋势时,对相关的影响会更大些.给出了实例.     

Extending dynamic convex risk measures from discrete time to continuous time: A convergence approach

We present an approach for the transition from convex risk measures in a certain discrete time setting to their counterparts in continuous time. The aim of this paper is to show that a large class of convex risk measures in continuous time can be obtained as limits of discrete time-consistent convex risk measures. The discrete time risk measures are constructed from properly rescaled (‘tilted’) one-period convex risk measures, using a d-dimensional random walk converging to a Brownian motion. Under suitable conditions (covering many standard one-period risk measures) we obtain convergence of the discrete risk measures to the solution of a BSDE, defining a convex risk measure in continuous time, whose driver can then be viewed as the continuous time analogue of the discrete ‘driver’ characterizing the one-period risk. We derive the limiting drivers for the semi-deviation risk measure, Value at Risk, Average Value at Risk, and the Gini risk measure in closed form.     

Tree-structured modelling of categorical predictors in generalized additive regression

Generalized linear and additive models are very efficient regression tools but many parameters have to be estimated if categorical predictors with many categories are included. The method proposed here focusses on the main effects of categorical predictors by using tree type methods to obtain clusters of categories. When the predictor has many categories one wants to know in particular which of the categories have to be distinguished with respect to their effect on the response. The tree-structured approach allows to detect clusters of categories that share the same effect while letting other predictors, in particular metric predictors, have a linear or additive effect on the response. An algorithm for the fitting is proposed and various stopping criteria are evaluated. The preferred stopping criterion is based on p values representing a conditional inference procedure. In addition, stability of clusters is investigated and the relevance of predictors is investigated by bootstrap methods. Several applications show the usefulness of the tree-structured approach and small simulation studies demonstrate that the fitting procedure works well.     

Stochastic games with metric state space

In this paper the stochastic two-person zero-sum game of Shapley is considered, with metric state space and compact action spaces. It is proved that both players have stationary optimal strategies, under conditions which are weaker than those ofMaitra/Parthasarathy (a.o. no compactness of the state space). This is done in the following way: we show the existence of optimal strategies first for the one-period game with general terminal reward, then for then-period games (n=1,2,...); further we prove that the game over the infinite horizon has a valuev, which is the limit of then-period game values. Finally the stationary optimal strategies are found as optimal strategies in the one-period game with terminal rewardv.     

Extension of multivariate regression trees to interval data. Application to electricity load profiling

Summary  Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent (or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the independent variables.     

Representation of time in mathematical programming modeling languages

Time-staged mathematical programming models have a planning horizon that is divided into a sequence of consecutive time periods. For the modeling of this sequence of time periods the use of calendars is proposed as an additional set concept for mathematical programming modeling languages. The definition of calendars is based on familiar notions such as set, ordering, interval length and functions. A calendar is an interval set and can be used to verify automatically the proper time referencing in stock balances. When a calendar is also a difference set, then backward and forward time referencing can be stated with the explicit use of time units. For models with a rolling horizon, concise and flexible ways to specify the structure of calendars are presented. The aggregation of raw data into model parameter values is supported by linking calendars that represent different time scales. The influence of the proposed calendar concept on the human ability to understand, maintain and verify models is analyzed throughout the paper on the basis of selected examples.     

Stochastic Stability of Some Mechanical Systems

We discuss the behavior, for large values of time, of two linear stochastic mechanical systems. The systems are similar mathematically in that they contain a white noise in their parameters. The initial data may be random as well but are independent of white noise. The expected energy is calculated in both cases. It is well known that for free nonstochastic mechanical systems with viscous damping, the energy approaches zero as time increases. We check that this behavior takes place for the stochastic systems under consideration in the case when the initial data are random but the parameters are not. When the parameters contain a random noise the expected energy may be infinite, approach zero, remain bounded, or increase with no bound. This regime is similar to but more interesting than the known regime for the solutions of differential equations with time dependent periodic coefficients that describes the behavior of a mechanical system with characteristics that are periodic functions of time. We give necessary and sufficient conditions for stability of both systems in terms of the structure of the set of roots of an auxiliary equation.