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非线性回归估计量的不对称性AI 解读

The least-squares estimators of nonlinear regression parameters are partitioned into three terms. Only the first is symmetrically distributed. Measures of asymmetry are derived from the other two which are valid for any sample size in that, if they are both zero for a given parameter, then its estimator is exactly symmetrically distributed. Asymptotic results are derived and we find that all odd central moments have a common factor when evaluated to first order. This factor…

替代性渐近最优检验及其在动态设定中的应用AI 解读

A method is presented for generating test statistics which share the same first order asymptotic optimality properties of the classical statistics. Generalizing Neyman's (1959) work, the linearised classical statistic tests restrictions in implicit function form using a parameter estimator which is consistent and asymptotically normally distributed under the alternative hypothesis. By judicious choice of estimator and form of restrictions at which to evaluate the statistic,…

随机变量集合内部依赖程度的度量AI 解读

To measure dependence in a set of random variables, a multivariate analog of maximal correlation is considered. This consists of transforming each of the variables so that the largest partial sums of the eigenvalues of the resulting correlation matrix is maximized. A "maximalized" measure of association obtained in this manner permits statements to be made about the strength of internal dependence exhibited by the random variables. It is shown, under a weak regularity…

识别时间序列中的简化结构AI 解读

This article studies how to identify hidden factors in multivariate time series process. This problem is important because, when the series are driven by a set of common factors, (a) a large number of parameters may be needed to obtain an adequate representation of the system and (b) the estimated parameters will be highly correlated. Therefore, a complex and badly defined relationship can appear when, in fact, a simpler and parsimonious model in terms of a few common…

椭圆分布误差下因子分析的峰度调整AI 解读

SUMMARY Simple corrections to normal theory tests of fit of the factor analysis model are provided for the situation where common factors have arbitrary distributions and errors have a distribution from the elliptical class. It is shown that normal theory estimators are asymptotically efficient in this situation and appropriate modifications for the asymptotic covariance matrix of estimators are provided. Factor analysis models with structured error covariance matrices are…

因子分析及相关模型中统计推断的稳健性AI 解读

A class of latent variable models which includes the unrestricted factor analysis model is considered. It is shown that minimum discrepancy test statistics and estimators derived under normality assumptions retain their asymptotic properties when the common factors are not normally distributed but the unique factors do have a multivariate normal distribution. The minimum discrepancy test statistics and estimators considered include the usual likelihood ratio test statistic…

非同步数据与收益的协方差因子结构AI 解读

ABSTRACT Evidence is presented that indicates that the standard estimator of the covariance matrix of daily returns provides a distorted view of the true covariance‐factor structure. An alternative estimator, based on a model of the price‐adjustment delay process, reveals roughly twice as much covariation in individual security returns. The number of factors identified also appears to increase when this estimator is employed. Since the linear space spanned by the estimated…

含共同因子的聚合所产生的含义AI 解读

Many observed macrovariables are simple aggregates over a large number of microunits. It is pointed out that the generating process of the macrovariables is largely determined by the common factors in the generating mechanisms of the microvariables, even though these factors may be very unimportant at the microlevel. It follows that macrorelationships are simpler than the complete microrelationships, but that empirical investigations of microrelationships may not catch those…

共同主成分模型的两种推广AI 解读

Under the common principal component model the covariance matrices Ψi, of k populations are assumed to have identical eigenvectors, that is, the same orthogonal matrix diagonalizes all Ψi, simultaneously. This paper modifies the common principal component model by assuming that only q out of p eigenvectors are common to all Ψi, while the remaining p–q elgenvectors are specific in each group. This is called a partial common principal component model. A related modification…

动态要素需求的估计AI 解读

Abstract Many recent empirical studies on inter‐related factor demands involve factor demand functions whose arguments depend on the researcher's arbitrary normalization rule. This paper shows how sensitive the estimated elasticities are to the various normalizations that are possible. Alternatives that are not dependent on a normalization are suggested.

非线性回归估计量的非对称性AI 解读

The least-squares estimators of nonlinear regression parameters are partitioned into three terms. Only the first is symmetrically distributed. Measures of asymmetry are derived from the other two which are valid for any sample size in that, if they are both zero for a given parameter, then its estimator is exactly symmetrically distributed. Asymptotic results are derived and we find that all odd central moments have a common factor when evaluated to first order. This factor…

球面角中心高斯分布的统计分析AI 解读

The angular central Gaussian distribution is an alternative to the Bingham distribution for modeling antipodal symmetric directional data. In this paper the statistical theory for the angular central Gaussian model is presented. Some topics treated are maximum likelihood estimation of the parameters, testing for uniformity and circularity, and principal components analysis. Comparisons to methods based upon the sample second moments are made via an example.

因子分析及相关模型中统计推断的稳健性AI 解读

A class of latent variable models which includes the unrestricted factor analysis model is considered. It is shown that minimum discrepancy test statistics and estimators derived under normality assumptions retain their asymptotic properties when the common factors are not normally distributed but the unique factors do have a multivariate normal distribution. The minimum discrepancy test statistics and estimators considered include the usual likelihood ratio test statistic…

共同主成分模型的两种推广AI 解读

Under the common principal component model the covariance matrices Ψi, of k populations are assumed to have identical eigenvectors, that is, the same orthogonal matrix diagonalizes all Ψi, simultaneously. This paper modifies the common principal component model by assuming that only q out of p eigenvectors are common to all Ψi, while the remaining p–q elgenvectors are specific in each group. This is called a partial common principal component model. A related modification…

摘要暂缺

抽样设计对主成分分析的影响AI 解读

Most sample surveys are multivariate and many lend themselves to multivariate methods of analysis. The most usual mode of such analysis is a standard statistical package, such as BMDP or SPSS, in which the multivariate analyses are based on the underlying assumption that the data are generated as independent observations from a common probability distribution. This assumption ignores the sample selection procedure involved in the survey, which leads to the following basic…

摘要暂缺

抽样设计对主成分分析的影响AI 解读

Most sample surveys are multivariate and many lend themselves to multivariate methods of analysis. The most usual mode of such analysis is a standard statistical package, such as BMDP or SPSS, in which the multivariate analyses are based on the underlying assumption that the data are generated as independent observations from a common probability distribution. This assumption ignores the sample selection procedure involved in the survey, which leads to the following basic…

共同主成分分析的渐近理论AI 解读

Under the common principal component model $k$ covariance matrices $\mathbf{\Sigma}_1,\cdots,\mathbf{\Sigma}_k$ are simultaneously diagonalizable, i.e., there exists an orthogonal matrix $\mathbf{\beta}$ such that $\mathbf{\beta'\Sigma_i\beta = \Lambda_i}$ is diagonal for $i = 1,\cdots, k$. In this article we give the asymptotic distribution of the maximum likelihood estimates of $\mathbf{\beta}$ and $\mathbf{\Lambda}_i$. Using these results, we derive tests for (a) equality…

匹配病例对照研究中的交叉乘积估计量AI 解读

This paper is devoted to a study of the unmatched cross-product estimator of relative risk in a I : M matched case-control study. It is shown that this estimator is consistent for the ‘case-adjusted relative risk’ in rare disease settings. The cross-product estimator has a much wider range of applicability than the classical matched methods, which require a conditionally independent binomial model with a common odds ratio. In general, the methods estimate different…

过度参数化结构模型的渐近理论AI 解读

A theory of overparameterized structural models is presented. In such a model some “redundant” parameters are involved; the parameter vector is not identified, and the information matrix is not nonsingular. The minimum discrepancy function (MDF) test statistic is shown to have an asymptotic chi-squared distribution almost everywhere for a wide class of discrepancy functions. Asymptotic distribution properties of the MDF estimators are investigated. The factor analysis model…

过度参数化结构模型的渐近理论AI 解读

A theory of overparameterized structural models is presented. In such a model some “redundant” parameters are involved; the parameter vector is not identified, and the information matrix is not nonsingular. The minimum discrepancy function (MDF) test statistic is shown to have an asymptotic chi-squared distribution almost everywhere for a wide class of discrepancy functions. Asymptotic distribution properties of the MDF estimators are investigated. The factor analysis model…

匹配病例对照研究中的交叉乘积估计量AI 解读

This paper is devoted to a study of the unmatched cross-product estimator of relative risk in a I : M matched case-control study. It is shown that this estimator is consistent for the ‘case-adjusted relative risk’ in rare disease settings. The cross-product estimator has a much wider range of applicability than the classical matched methods, which require a conditionally independent binomial model with a common odds ratio. In general, the methods estimate different…

主成分分析中的影响诊断AI 解读

In linear regression, the theoretical influence function and the various sample versions of it have an established place as diagnostic tools. These same functions are developed here to provide methods for the detection of influential observations in principal components analysis. The perturbation theory of real symmetric matrices unifies this development. Some interesting points of contrast with the regression case are noted and explained theoretically.