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回归模型的非参数检验AI 解读

In this paper we study a marked empirical process based on residuals. Results on its large-sample behavior may be used to provide nonparametric full-model checks for regression. Their decomposition into principal components gives new insight into the question: which kind of departure from a hypothetical model may be well detected by residual-based goodness-of-fit methods? The work also contains a small simulation study on straight-line regression.

摘要暂缺

杂论：利用高斯图规则识别单因子模型AI 解读

We state a sufficient condition for the global identification of a single-factor model when some conditional associations among residuals are allowed The condition relies on the structure of the conditional independence graph of the observed variable given the latent factor, and can be derived using graphical rules.

主成分分析中的局部影响AI 解读

Based on the definition of generalised influence function and generalised Cook statistic, local influence of small perturbations on the eigenvalues and eigenvectors of a covariance matrix are studied for population and sample versions. The results based on the correlation matrix are also derived and some related topics are discussed. Finally, an example is used for illustration.

空间疾病发生率的回归方法：估计函数途径AI 解读

Epidemiologists commonly study the geographical variation of disease rates to generate and refine testable hypotheses regarding etiology. Poisson regression is often used to estimate the parameters that characterize the effects of risk factors on disease. Hierarchical models have been proposed to utilize spatial locations and neighbors as surrogates for unknown or unmeasured risk factors in the analysis of disease rates. Although hierarchical models are useful in modeling…

简化单调回归方法AI 解读

Medical researchers often desire to categorize patients into monotonic response groups based on the relationship between continuous variables. Isotonic regression fits consist of level sets of increasing value, for which the estimated response is constant. However, the number of level sets obtained is often large, preventing simple description. This article introduces two new nonparametric methods called reduced isotonic regression and reduced monotonic regression, the…

简化单调回归方法AI 解读

Medical researchers often desire to categorize patients into monotonic response groups based on the relationship between continuous variables. Isotonic regression fits consist of level sets of increasing value, for which the estimated response is constant. However, the number of level sets obtained is often large, preventing simple description. This article introduces two new nonparametric methods called reduced isotonic regression and reduced monotonic regression, the…

空间疾病发生率的回归方法：估计函数途径AI 解读

Epidemiologists commonly study the geographical variation of disease rates to generate and refine testable hypotheses regarding etiology. Poisson regression is often used to estimate the parameters that characterize the effects of risk factors on disease. Hierarchical models have been proposed to utilize spatial locations and neighbors as surrogates for unknown or unmeasured risk factors in the analysis of disease rates. Although hierarchical models are useful in modeling…

协整时间序列的主成分分析AI 解读

This paper considers the analysis of cointegrated time series using principal components methods. These methods have the advantage of requiring neither the normalization imposed by the triangular error correction model nor the specification of a finite-order vector autoregression. An asymptotically efficient estimator of the cointegrating vectors is given, along with tests forcointegration and tests of certain linear restrictions on the cointegrating vectors. An illustrative…

游程与最长游程检验：有限马尔可夫链嵌入方法AI 解读

In this article, under very general assumptions on Bernoulli trials, a simple numerical method based on the finite Markov chain imbedding technique is systematically developed to determine (a) the joint distribution of the number of success runs and the number of successes; (b) the conditional distribution of the number of success runs given the number of successes, that is, the exact distribution of the success runs test statistic; (c) the joint distribution of the length…

预测振荡模式：随机场时空分解方法的综合AI 解读

Spatial-temporal decompositions of climatologic fields have been obtained using a range of techniques, including principal component analysis (PCA) and principal oscillation patterns (POPS). PCA decompositions are forced to be correlated to the original field, but they may not capture interesting aspects of temporal variation. On the other hand, POPS decompositions focus on temporal variation but are not forced to correlate to the field. Here we introduce a hybrid of these…

预测振荡模式：随机场时空分解方法的综合AI 解读

Spatial-temporal decompositions of climatologic fields have been obtained using a range of techniques, including principal component analysis (PCA) and principal oscillation patterns (POPS). PCA decompositions are forced to be correlated to the original field, but they may not capture interesting aspects of temporal variation. On the other hand, POPS decompositions focus on temporal variation but are not forced to correlate to the field. Here we introduce a hybrid of these…

连续统回归并不总是连续AI 解读

SUMMARY Continuum regression (CR) is regression of a response y on that linear combination t γ of explanatory variables which maximizes r 2(y, t) var(t)γ, where γ is chosen from a continuum of candidates. CR includes several common methods as special cases: ordinary least squares, principal component regression, partial least squares and a modified ridge regression. We demonstrate that CR, despite its name, can yield a predictor that is a discontinuous function of γ and of…

The problem of estimating the number of binary responses that can be attributed to high levels of a continuous covariate is studied. The distribution of the covariate in the population is combined with the relative odds of response to produce a function, areas under which represent numbers of responses attributable to covariate values in that range. A combination of parametric and nonparametric smoothing is proposed. Consistent estimates of variance are developed as well as…

全信息项目因子模型的拟合与桥接抽样的实证考察AI 解读

Based on item response theory, Bock and Aitken introduced a method of item factor analysis, termed full-information item factor (FIIF) analysis by Bartholomew because it uses all distinct item response vectors as data. But a limitation of their fitting algorithm is its reliance on fixed-point Gauss—Hermite quadrature, which can produce appreciable numerical errors, especially in high-dimension problems. The first purpose of this article is to offer more reliable methods by…

非线性协方差结构模型估计量的小样本性质AI 解读

This study examines the small sample properties of generalized method of moments (GMM) and maximood likelihood estimators of nonlinear models of covariance structure. It considers the properties of estimates for a simple factor model, the Hall and Mishkin (1982) model of consumption and income, and a simple structural vector-autoregression-type error model. This analysis establishes three basic results. First, optimally weighted GMM estimation yields some biased parameter…

医学研究的统计方法AI 解读

Statistics in medicine the design of medical investigations - clinical trials and other methods measurement in medicine statistical inference regression analysis repeated measures analysis of variance and the analysis of covariance crossover designs the analysis of survival data multivariate data and principal components analysis statistical methods for classification - cluster analysis and assignment techniques time series analysis the analysis of observational techniques.…

通过范数选择实现平滑函数主成分分析AI 解读

The principal components analysis of functional data is often enhanced by the use of smoothing. It is shown that an attractive method of incorporating smoothing is to replace the usual $L^2$-orthonormality constraint on the principal components by orthonormality with respect to an inner product that takes account of the roughness of the functions. The method is easily implemented in practice by making use of appropriate function transforms (Fourier transforms for periodic…

摘要暂缺

含随机波动率与跳跃的连续时间套利定价模型AI 解读

We formulate and test a continuous-time asset-pricing model using U.S. equity market data. We assume that stock returns are driven by common factors including random jump-size Poisson processes and Brownian motions with stochastic volatility. The model places overidentifying restrictions on the mean returns, allowing one to identify risk-neutral probability distributions useful in pricing derivative securities. We test for the restrictions and decompose moments of the asset…

利用全局优势比分析双变量有序数据AI 解读

A moment method is proposed for regression analysis of bivariate ordered categorical data using the global odds ratio as the measure of association. For modeling the margins, this method utilizes the stochastic ordering implicit in the data. This method allows for covariate effects in modeling the association between responses. An application of the proposed model is illustrated using the ophthalmological data from the Wisconsin Epidemiologic Study of Diabetic Retinopathy…

在函数型主成分分析中纳入参数效应AI 解读

SUMMARY The ideas of functional principal component analysis are extended to deal with data that are hybrids of ‘functional’ and ‘parametric’ effects. The parametric effects may be more general than just the addition of a multiple of a given function. A detailed development is given in the case of shifts of the time axis for functions observed on a periodic interval, and some remarks are made for the extension to a far more general case. Given data, a Procrustes fitting…

二次ARCH模型AI 解读

We introduce a new model for time-varying conditional variances as the most general quadratic version possible within the ARCH class. Hence, it encompasses all the existing restricted quadratic variance functions. Its properties are very similar to those of GARCH models, but avoids some of their criticisms. In univariate applications to daily U.S. and monthly U.K. stock market returns, QARCH adequately represents volatility and risk premia. QARCH is easy to incorporate in…

使用 EQS 建模协方差与潜变量AI 解读

The Ideas of Covariance and Covariance Structure. Writing a Simple EQS Program. Statistical Modelling in EQS. Confirmatory Factor Analysis Models. Multitrait-Multimethod and Multiple Cause-Multiple Indicator Models. Models for Longitudinal Data. Simultaneous Analysis of Two or More Groups. Practical Problems.