A note on the projection properties of Hadamard matrices obtained by the first Payley construction

We show that the projections on four factors of an arbitrary orthogonal array of strength 2 allow the estimation of main effects and two-factor interactions when all other effects are assumed to be zero, if those projections satisfy the bounds given by Weils theorem. The only exceptions are the Hadamard matrices of orders 16 and 24. A consequence is again the estimability of main effects and two-factor interactions for the projections on four factors of the first Payley construction for arbitrary run size.     

Optimality results on orthogonal arrays plus <Emphasis Type="Italic">p</Emphasis> runs for <Emphasis Type="Italic">s</Emphasis><Superscript><Emphasis Type="Italic">m</Emphasis></Superscript> factorial experiments

The optimality of designs obtained by adding p runs to an orthogonal array is studied for experiments involving m factors each at s levels. The optimality criterion used here, is the Type 1 criterion due to Cheng (1978) which is an extension of Kiefer (1975) universal optimality criterion. Unlike what happens with orthogonal array plus one run designs, the behavior of designs obtained via augmentation of an orthogonal array by p runs depends on the particular runs added.     

A new approach in constructing orthogonal and nearly orthogonal arrays

Orthogonal arrays have been constructed by a number of mathematical tools such as orthogonal Latin squares, Hadamard matrices, group theory and finite fields. Wang and Wu (1992) proposed the concept of a nearly orthogonal array and found a number of such arrays with high efficiency. In this paper we propose some criteria for non-orthogonality and two algorithms for the construction of orthogonal and nearly orthogonal arrays evincing higher efficiency than that obtained by Wang and Wu. Received: September 1999     

Small asymmetric fractional factorial plans for main effects and specified two-factor interactions

Fractional factorial plans represented by orthogonal arrays of strength two are known to be optimal in a very strong sense under a model that includes the mean and all the main effects, when all interactions are assumed to be absent. When a fractional factorial plan given by an orthogonal array of strength two is not saturated, one might think of entertaining some two-factor interactions also in the model. In such a situation, it is of interest to examine which of the two-factor interactions can be estimated via a plan represented by an orthogonal array, as also to study the overall efficiency of the plan when some interactions are in the model alongwith the mean and all main effects. In this paper, an attempt has been made to examine these issues by considering some practically useful plans for asymmetric (mixed level) factorials with small number of runs.     

Method of moments estimation of GO-GARCH models

We propose a new estimation method for the factor loading matrix in generalized orthogonal GARCH (GO-GARCH) models. The method is based on eigenvectors of suitably defined sample autocorrelation matrices of squares and cross-products of returns. The method is numerically more attractive than likelihood-based estimation. Furthermore, the new method does not require strict assumptions on the volatility models of the factors, and therefore is less sensitive to model misspecification. We provide conditions for consistency of the estimator, and study its efficiency relative to maximum likelihood estimation using Monte Carlo simulations. The method is applied to European sector returns.     

Analysis of high dimensional multivariate stochastic volatility models

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time series models with time varying correlations. The model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate stochastic volatility model. A unified analysis of the model, and its special cases, is developed that encompasses estimation, filtering and model choice. The centerpieces of the estimation algorithm (which relies on MCMC methods) are: (1) a reduced blocking scheme for sampling the free elements of the loading matrix and the factors and (2) a special method for sampling the parameters of the univariate SV process. The resulting algorithm is scalable in terms of series and factors and simulation-efficient. Methods for estimating the log-likelihood function and the filtered values of the time-varying volatilities and correlations are also provided. The performance and effectiveness of the inferential methods are extensively tested using simulated data where models up to 50 dimensions and 688 parameters are fit and studied. The performance of our model, in relation to various multivariate GARCH models, is also evaluated using a real data set of weekly returns on a set of 10 international stock indices. We consider the performance along two dimensions: the ability to correctly estimate the conditional covariance matrix of future returns and the unconditional and conditional coverage of the 5% and 1% value-at-risk (VaR) measures of four pre-defined portfolios.     

基于均匀圆阵的空间谱估计算法研究

空间谱估计是对空间信号的波到达方向进行超分辨估计。而多重信号分类(MUSIC)算法基于对阵列天线输出信号的自相关矩阵进行特征值分解,通过谱峰搜索获得信号的空间谱。本文基于九元均匀圆阵对MUSIC算法进行了仿真,证明算法对模拟信号以及实际信号都具有良好的测向能力,为工程实现提供具有重要参考价值的技术基础。     

Een algemene methode tot leemtevulling

An exposition of the missing plot technique often applied in analysis of variance is given in very general terms.
Non-orthogonality mostly implies heavy computations. If the scheme of observations is almost orthogonal this technique, however, supplies in a simple way unbiassed and efficient estimates of the expectation values which occur in a linear hypothesis underlying an analysis of variance. Moreover the correct residual sum of squares required for a test or a confidence interval estimation is obtained without difficulty.
A correct test of an effect or an interaction will be provided by two estimates, the first under the null-hypothesis, the second under the alternative hypothesis. In the case of non-orthogonality this may imply two separate applications of the discussed technique. The difference between the two residual sums of squares will be used for the numerator of a valid F-criterion.
The technique is illustrated by an example.     

Credibility estimators with dependence structure over risks and time under balanced loss function

In this paper, we study the Bühlmann credibility model with constant interest rate and equal dependence structure over risks and time under balanced loss function. By means of orthogonal projection, the inhomogeneous and homogeneous credibility premium estimators are derived, which extend those for the existing models to slightly more general versions. Finally, we investigate the estimation of the structure parameters and present a numerical example to show the effectiveness of the inhomogeneous estimator.     

Main effects as a by-product in a fixed effect 2 × 2 design,analyzed with ANOVA

To study interaction effects, two sets of data are created for fixed effect ANOVA, both with combinatory effects of the two factors. In the first, both factors and their interaction contribute independently and directly to the dependent variable. In the second, each factor contributes indirectly to the dependent score. Data created with the first model can be analyzed flawlessly. The second often show relatively large main effects and relatively small interaction effects, and as a consequence the interaction effect may be rejected. Even when the dependent variable results solely from the multiplication of both factor scores, highly significant main effects can be obtained, while the interaction effect remains insignificant. Although mathematically correct, the relative contributions of the main effects are in that case difficult to interpret.     

Estimation and simulation of industry factor demand equations

A factor demand model derived from the Generalized Leontief Cost Function is outlined. The model is estimated for 53 U.S. industries which together make up the entire U.S. economy. The purpose of the model is to provide long-run forecasts of equipment investment and labor productivity within the context of an input-output forecasting model. Extensive discussion is devoted to selection of functional form, modeling of dynamics, and estimation technique, with special emphasis upon the considerations which uniquely confront the forecaster. An array of a priori constraints are imposed upon the estimation, reflecting a body of empirical evidence and theoretical requirements. In addition, elasticity estimates are reported, as well as evidence on the short-run simulation performance of the mode.     

The Alpha Risk of Taguchi Method for LTB Type Quality Characteristic with L<Subscript>8</Subscript>

In Taguchi’s parameter design, the significant parameter levels are found by maximising the signal-to-noise ratio of the quality characteristic. In the analysis of variance (ANOVA) of signal-to-noise ratio, the combination of column effects to better estimate error variance is referred to as pooling. Taguchi has suggested the strategy of pooling up. When using the pooling-up strategy, there will be a tendency to make the alpha mistake more often. In this paper, it is assumed that (1) the quality chartacteristic is normally distributed and (2) the mean and standard deviation of each factor level combination are equal, then the null hypothesis should be no significant factor. Thus, the alpha risk is that some factors are misidentified as significant factors. The purpose of this paper is to investigate the alpha risk of the Taguchi method for the-larger-the-better (LTB) type problem with orthogonal array, L₈, by simulation. The results show that the alpha risk is very high.     

满山香提取工艺的研究

文章采用正交试验设计方法,选择药材的破碎度、乙醇倍数、乙醇浓度和渗漉速度4个因素,每个因素选3个水平对满山香渗漉提取工艺进行最佳工艺条件的研究。实验结果表明,满山香细粉的颗粒直径为1～2mm、乙醇量为药材量的6倍、乙醇浓度为60%、渗漉速度4～5ml/min为最佳提取工艺参数。该工艺方法对满山香的提取及开发应用具有一定的指导意义。     

Identification and estimation of econometric models with group interactions,contextual factors and fixed effects

This paper considers identification and estimation of structural interaction effects in a social interaction model. The model allows unobservables in the group structure, which may be correlated with included regressors. We show that both the endogenous and exogenous interaction effects can be identified if there are sufficient variations in group sizes. We consider the estimation of the model by the conditional maximum likelihood and instrumental variables methods. For the case with large group sizes, the possible identification can be weak in the sense that the estimates converge in distribution at low rates.     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

GMM estimation of social interaction models with centrality

This paper considers the specification and estimation of social interaction models with network structures and the presence of endogenous, contextual, correlated, and group fixed effects. When the network structure in a group is captured by a graph in which the degrees of nodes are not all equal, the different positions of group members as measured by the Bonacich (1987) centrality provide additional information for identification and estimation. In this case, the Bonacich centrality measure for each group can be used as an instrument for the endogenous social effect, but the number of such instruments grows with the number of groups. We consider the 2SLS and GMM estimation for the model. The proposed estimators are asymptotically efficient, respectively, within the class of IV estimators and the class of GMM estimators based on linear and quadratic moments, when the sample size grows fast enough relative to the number of instruments.     

Cross-industry asset allocation with the spatial interaction on multiple risk transmission channels

Taking into consideration the real link and information risk transmission channels, we used a spatial econometric approach to construct an economic distance-based spatial weight matrix, which can capture the spatial interaction across industries, and built a return estimation model with spatial interaction using the matrix. On this basis, we derived the covariance matrix and constructed the cross-industry asset allocation model. The empirical results showed that 1) the spatial interaction has a strong explanatory power to return and integrating the spatial interaction on multiple risk transmission channels can improve the effectiveness of the return estimation model; 2) the covariance matrix includes unsystematic risk (idiosyncratic risk) and systematic risk (market risk and cross-industry spillover risk); 3) the asset allocation model with spatial interaction can improve the performance of the portfolio and provide a valuable reference for investors' risk management and investment decision.     

The Optimality of Single-group Designs for Certain Mixed Models

In this paper optimal designs for the estimation of the fixed effects (population parameters) in a certain class of mixed models are investigated. Two classes of designs are compared: the class of single-group designs, where all individuals are observed under the same approximate design, and the class of more-group designs with the same mean number of observations per individual as before, where each individual can be observed under a different approximate design. It is shown that any design that is Φ-optimal in the class of single-group designs is also Φ-optimal in the larger class of more-group designs. The considered optimality criteria only have to satisfy mild assumptions, which is eg the case for the D-criterion and all linear criteria.     

浅析产品设计中的情感付诸

情感是产品设计中不可忽略的产物,设计师必须在产品设计中付诸情感。文章探讨了情感沟通的具体内涵,揭示了以用户需求为中心,通过交互式设计来挖掘情感的相关方法,详细论述了通过注重人性化设计、形式要素以及产品功能来深化情感的若干策略。     

Consistent factor estimation in dynamic factor models with structural instability

This paper considers the estimation of approximate dynamic factor models when there is temporal instability in the factor loadings. We characterize the type and magnitude of instabilities under which the principal components estimator of the factors is consistent and find that these instabilities can be larger than earlier theoretical calculations suggest. We also discuss implications of our results for the robustness of regressions based on the estimated factors and of estimates of the number of factors in the presence of parameter instability. Simulations calibrated to an empirical application indicate that instability in the factor loadings has a limited impact on estimation of the factor space and diffusion index forecasting, whereas estimation of the number of factors is more substantially affected.