论文检索

摘要暂缺

基于最小消息长度估计的单因子分析AI 解读

SUMMARY The minimum message length (MML) technique is applied to the problem of estimating the parameters of a multivariate Gaussian model in which the correlation structure is modelled by a single common factor. Implicit estimator equations are derived and compared with those obtained from a maximum likelihood (ML) analysis. Unlike ML, the MML estimators remain consistent when used to estimate both the factor loadings and the factor scores. Tests on simulated data show the…

估计矩阵的秩与定性检验：因子、状态空间及 ARMA 应用AI 解读

Consider any consistent, asymptotically normal estimate ǎ of an arbitrary rectangular or square matrix A. This article derives an explicit test for the rank of A and a related test of (semi) definiteness of A. Potential applications include testing for identification of structural models, testing for the number of state variables in state-space models (including tests for the order of autoregressive moving average (ARMA) processes), consumer demand analysis applications, and…

多变量家庭数据的主成分分析AI 解读

The use of a principal component analysis is considered for multivariate data on families with different numbers of siblings. The coefficients in principal components are given as the eigenvectors of the weighted sums of squares and products matrix from the sibling data. Asymptotic distributions of the eigenvalues and eigenvectors of the estimated covariance matrix are obtained for an elliptical population. Asymptotic distributions of statistics associated with reduction of…

因子 GARCH 模型的替代估计量：蒙特卡洛比较AI 解读

Abstract This paper proposes four estimators for factor GARCH models: two‐stage univariate GARCH (2SUE), two‐stage quasi‐maximum likelihood (2SML), quasi‐maximum likelihood with known factor weights (RMLE), quasi‐maximum likelihood with unknown factor weights (MLE). A Monte‐Carlo study is designed for bivariate one‐factor GARCH models to examine the finite sample properties. Results are presented for biases, ratios of standard errors to standard deviations, ratios of…

非线性多元分析AI 解读

Conventions and Controversies in Multivariate Analysis. Coding of Categorical Data. Homogeneity Analysis. Nonlinear Principal Components Analysis. Nonlinear Generalized Canonical Analysis. Nonlinear Canonical Correlation Analysis. Asymmetric Treatment of Sets: Some Special Cases, Some Future Programs. Multidimensional Scaling and Correspondence Analysis. Models as Gauges for the Analysis of Binary Data. Reflections on Restrictions. Nonlinear Multivariate Analysis: Principles…

生物学中的随机化与蒙特卡洛方法AI 解读

Part 1 Randomization tests and confidence intervals: the idea of a randomization test examples of a randomization test aspects of randomization testing raised by the examples confidence intervals from randomization. Part 2 Monte Carlo and other computer intensive methods: Monte Carlo tests jackknifing bootstrapping bootstrap tests of significance and confidence intervals. Part 3 Some general considerations: power determining how many randomizations are needed determining a…

摘要暂缺

多元家庭数据的主成分分析AI 解读

The use of a principal component analysis is considered for multivariate data on families with different numbers of siblings. The coefficients in principal components are given as the eigenvectors of the weighted sums of squares and products matrix from the sibling data. Asymptotic distributions of the eigenvalues and eigenvectors of the estimated covariance matrix are obtained for an elliptical population. Asymptotic distributions of statistics associated with reduction of…

市场模型中贝塔系数的时间序列性质检验AI 解读

Understanding the stochastic behavior of beta is fundamental to portfolio risk management. We evaluate the time series properties of beta for oil industry stocks, machinery industry stocks, and a random sample of 100 stocks. Daily returns for stocks in each of these industries and the random sample are obtained from the Center for Research in Security Prices tapes for the period July 1962 through December 1986 and are grouped into 98 quarter-year, and also 49 half-year,…

市场模型中贝塔系数的时间序列性质考察AI 解读

Understanding the stochastic behavior of beta is fundamental to portfolio risk management. We evaluate the time series properties of beta for oil industry stocks, machinery industry stocks, and a random sample of 100 stocks. Daily returns for stocks in each of these industries and the random sample are obtained from the Center for Research in Security Prices tapes for the period July 1962 through December 1986 and are grouped into 98 quarter-year, and also 49 half-year,…

多组共同主成分子空间的若干检验AI 解读

SUMMARY An approximate test, based on sample eigenprojections, is obtained for testing the hypothesis that the subspaces spanned by the first m principal components of several different covariance matrices are identical. This procedure can fairly easily be extended to an analogous test of hypothesis concerning correlation matrices. Extensions to a robust principal components analysis are also considered.

相关矩阵特定主成分的假设检验AI 解读

In the application of principal components analysis it is common to replace an observed sample principal component vector by another vector closely resembling the sample vector but which is easier to use or interpret. A useful test of hypothesis in this case is one that specifies the true ith principal component. In this article we obtain an asymptotically chi-squared procedure suitable for testing such a hypothesis when the principal components analysis is performed on a…

相关矩阵中特定主成分的检验AI 解读

In the application of principal components analysis it is common to replace an observed sample principal component vector by another vector closely resembling the sample vector but which is easier to use or interpret. A useful test of hypothesis in this case is one that specifies the true ith principal component. In this article we obtain an asymptotically chi-squared procedure suitable for testing such a hypothesis when the principal components analysis is performed on a…

探索性因子分析中一种非迭代估计量的渐近分布AI 解读

This paper presents the asymptotic distribution of Ihara and Kano's noniterative estimator of the uniqueness in exploratory factor analysis. When the number of factors is overestimated, the estimator is not a continuous function of the sample covariance matrix and its asymptotic distribution is not normal, but the consistency holds. It is also shown that the first-order moment of the asymptotic distribution does not exist.

多个群组共同主成分子空间的检验AI 解读

SUMMARY An approximate test, based on sample eigenprojections, is obtained for testing the hypothesis that the subspaces spanned by the first m principal components of several different covariance matrices are identical. This procedure can fairly easily be extended to an analogous test of hypothesis concerning correlation matrices. Extensions to a robust principal components analysis are also considered.

非均匀人口中的空间聚集检测AI 解读

SUMMARY A new method for detecting spatial clustering of events in populations with non-uniform density is proposed. The method is based on selecting controls from the population at risk and computing interpoint distances for the combined sample. Nonparametric tests are developed which are based on the number of cases among the k nearest neighbours of each case and the number of cases nearer than the k nearest control. The performance of these tests is evaluated analytically…

广泛因子分析模型的渐近卡方检验AI 解读

Three types of asymptotic $\chi^2$ goodness-of-fit tests derived under the normal assumption have been used widely in factor analysis. Asymptotic behavior of the test statistics is investigated here for the factor analysis model with linearly or nonlinearly restricted factor loadings under weak assumptions on the factor vector and the error vector. In particular the limiting $\chi^2$ result for the three tests is shown to hold for the factor vector, either fixed or random…

股票收益中的异方差AI 解读

ABSTRACT We use predictions of aggregate stock return variances from daily data to estimate time‐varying monthly variances for size‐ranked portfolios. We propose and estimate a single factor model of heteroskedasticity for portfolio returns. This model implies time‐varying betas. Implications of heteroskedasticity and time‐varying betas for tests of the capital asset pricing model (CAPM) are then documented. Accounting for heteroskedasticity increases the evidence that…

重新审视残差风险AI 解读

The Capital Asset Pricing Model in conjunction with the usual market model assumptions implies that well-diversified portfolios should be mean variance efficient and, hence, betas computed with respect to such indices should completely explain expected returns on individual assets. In fact, there is now a large body of evidence indicating that the market proxies usually employed in empirical tests are not mean variance efficient. Moreover, there is considerable evidence…

因子 ARCH 协方差结构下的资产定价AI 解读

In this paper we suggest using the FACTOR-ARCH model as a parsimonious structure for the conditional covariance matrix of asset excess returns. This structure allows us to study the dynamic relationship between asset risk premia and volatilities in a multivariate system. One and two FACTOR-ARCH models are succussfully applied to pricing of Treasury bills. The results show stability over time, pass a variety of diagnostic tests, and compare favorably with previous empirical…

ACE 类方法的一致性AI 解读

The ACE (alternating conditional expectations) algorithm developed by Breiman and Friedman is an iterative method for finding optimal transformations of variables in multiple regression. Recently, several authors have extended ACE to discriminant analysis, time series and principal components. The central idea of ACE and of each of these extensions is that an optimal transformation $\phi^\ast$ minimizes a squared error-related functional over a Hilbert space, subject to…

均衡中的家庭选择AI 解读

This paper is an empirical investigation of equilibrium restrictions on household consumption and male labor supply. It exploits a simple factor structure, rationalized by two assumptions, that household allocations are Pareto optimal and that the labor market is competitive. The paper estimates household preferences, and tests how well this parsimonious factor structure represents panel data on married couples and time series data on asset returns. Most of the estimates are…

广义线性回归的主成分估计AI 解读

The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical option to classical least-squares linear model building. We consider the specific problem of generalized linear regression utilizing a set of continuous explanatory variables to model an exponential family response. It is the objective of this paper to develop and present an asymptotically biased principal component parameter estimation technique, as an option to traditional maximum…