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近非平稳一阶自回归模型的参数推断AI 解读

The finite sample behaviour of the score function in a first-order autoregression is investigated. An object is to interpret the transition of the distribution of the score when the model approaches a nonstationary situation with a unit root. A framework is presented wherein the nonnormality of the score is clearly seen. Also, a simple interpretation for the behaviour of the distribution in terms of two chi-squared variables is given. Approximate finite sample distributions…

近非平稳一阶自回归模型的参数推断AI 解读

The finite sample behaviour of the score function in a first-order autoregression is investigated. An object is to interpret the transition of the distribution of the score when the model approaches a nonstationary situation with a unit root. A framework is presented wherein the nonnormality of the score is clearly seen. Also, a simple interpretation for the behaviour of the distribution in terms of two chi-squared variables is given. Approximate finite sample distributions…

基于 ARIMA 模型的季节调整方法AI 解读

This article proposes a model-based procedure to decompose a time series uniquely into mutually independent additive seasonal, trend, and irregular noise components. The series is assumed to follow the Gaussian ARIMA model. Properties of the procedure are discussed and an actual example is given.

多元时间序列建模及应用AI 解读

An approach to the modeling and analysis of multiple time series is proposed. Properties of a class of vector autoregressive moving average models are discussed. Modeling procedures consisting of tentative specification, estimation, and diagnostic checking are outlined and illustrated by three real examples.

摘要暂缺

时间序列中的隐含周期性自回归移动平均模型AI 解读

Some properties of a class of periodic models for characterizing seasonal time series are explored. The relationships between periodic models and multiple autoregressive-moving average models are developed and used to gain insight into the behaviour of periodic models. In particular it is shown how homogeneous autoregressive-moving average models may be mistakenly specified for series in which periodic properties are present. Consequences of such misspecification on…

时间序列分解的若干考察AI 解读

Suppose that an observable Gaussian time series Z1 can be written as the sum of an unobservable signal component Tt and a white noise component et. This paper proposes a procedure to estimate the Tt component uniquely by maximizing the variance of et with respect to a known model for Zt. Properties of this procedure are discussed and a comparison is made with a number of smoothing and filtering procedures commonly used in practice.

关于时间序列分解的若干考察AI 解读

Suppose that an observable Gaussian time series Z1 can be written as the sum of an unobservable signal component Tt and a white noise component et. This paper proposes a procedure to estimate the Tt component uniquely by maximizing the variance of et with respect to a known model for Zt. Properties of this procedure are discussed and a comparison is made with a number of smoothing and filtering procedures commonly used in practice.

多变量时间序列的典型分析AI 解读

This paper proposes a canonical transformation of a k-dimensional stationary autoregressive process. The components of the transformed process are ordered from least to most predictable. The least predictable components are often nearly white noise which can reflect stable contemporaneous relationships among the original variables. The most predictable can be nearly nonstationary representing the dynamic growth characteristic of the series. The method is illustrated with a…

多元时间序列的典型分析AI 解读

This paper proposes a canonical transformation of a k-dimensional stationary autoregressive process. The components of the transformed process are ordered from least to most predictable. The least predictable components are often nearly white noise which can reflect stable contemporaneous relationships among the original variables. The most predictable can be nearly nonstationary representing the dynamic growth characteristic of the series. The method is illustrated with a…

季节时间序列分解：Census X-11 程序的模型解释AI 解读

This paper shows that the linear filter version of the Census X-11 program for time-series decomposition can be approximately justified in terms of an additive model with stochastic trend, seasonal and noise components. Optimal estimates of the trend and seasonal components are obtained from the model and found to be in close agreement with the corresponding estimates for the Census procedure. This approach makes it possible to assess the appropriateness of the Census…

时间聚合对两个时间序列变量动态关系的影响AI 解读

This paper considers the effect of temporal aggregation on the dynamic relationships between two discrete time series variables. Given the model in terms of some basic time unit, the corresponding model for the aggregates is obtained. Examples are given showing that temporal aggregation can lead to a substantial loss in parameter estimation while the loss in prediction efficiency is much less severe.

时间序列时间聚合的渐近行为AI 解读

The problem of modelling and forecasting temporal aggregates of time series is discussed. Let zt be the basic series and xT be the m-component temporal aggregatos. Forecasts of future xT may be constructed from data on (i) zt or (ii) xT. It is shown that, for large m, there is no gain in using the basic data if zt is stationary, but considerable gain can be obtained when zt is nonstationary.

相关随机效应分析：含两个随机分量的线性模型AI 解读

Linear regression models with two random components, one of which is time correlated, are analysed from a Bayesian viewpoint. The problem of making inferences about the parameters when the time correlated component is stationary is discussed. Necessary modifications needed for nonstationary and explosive models are indicated. The analysis is illustrated by a numerical example showing that the time series component of the data can exert a strong influence in determining the…

相关随机效应分析：含两个随机分量的线性模型AI 解读

Linear regression models with two random components, one of which is time correlated, are analysed from a Bayesian viewpoint. The problem of making inferences about the parameters when the time correlated component is stationary is discussed. Necessary modifications needed for nonstationary and explosive models are indicated. The analysis is illustrated by a numerical example showing that the time series component of the data can exert a strong influence in determining the…

用有序线性无偏最小方差估计量研究位置参数推断的稳健性AI 解读

Coefficients for computing ordered linear unbiased minimum variance (OLUMV) estimators of the location and scale parameters have been obtained for selected members of a class of symmetric power distributions which includes the normal, double exponential, and as a limiting case, the rectangular. Two examples are given illustrating how these coefficients may be used to study the sensitivity of inferences concerning location parameters to departure from normality. The first…

用OLUMV估计量研究对称分布类位置参数推断的稳健性AI 解读

Coefficients for computing ordered linear unbiased minimum variance (OLUMV) estimators of the location and scale parameters have been obtained for selected members of a class of symmetric power distributions which includes the normal, double exponential, and as a limiting case, the rectangular. Two examples are given illustrating how these coefficients may be used to study the sensitivity of inferences concerning location parameters to departure from normality. The first…

特征根与特征向量的贝叶斯估计：以二元正态分布为重点AI 解读

This paper discusses from a Bayesian viewpoint some aspects of the estimation of latent roots and vectors of the covariance matrix of the bivariate normal distribution. The joint distribution of (i) the angle of the canonical transformation, and (ii) the ratio of the larger root to the total variance is considered in detail and illustrated by an example. Also discussed is the problem of making inferences about the larger roots, and several simple approximations to the…

特征根与特征向量的贝叶斯估计：以二元正态分布为重点AI 解读

This paper discusses from a Bayesian viewpoint some aspects of the estimation of latent roots and vectors of the covariance matrix of the bivariate normal distribution. The joint distribution of (i) the angle of the canonical transformation, and (ii) the ratio of the larger root to the total variance is considered in detail and illustrated by an example. Also discussed is the problem of making inferences about the larger roots, and several simple approximations to the…

单向随机效应模型中均值的贝叶斯估计AI 解读

The problem of estimating the means in the one-way random effect model yjk = θj+ejk is considered from a Bayesian viewpoint. Posterior distributions of the θj are obtained under the assumption that the θj are independently drawn from a Normal population N(θ, σ22) and that the ejk are independent random errors having a N(0, σ21) distribution. It is shown that the posterior distributions of the θj are clustered more closely together than are the corresponding distributions for…

若干异常值问题的贝叶斯方法AI 解读

The problem of outlying observations is considered from a Bayesian viewpoint. We suppose that each of the observations in an experiment in an experiment may come from either a ‘good’ run or a ‘bad’ run. By specifying the models corresponding to good and bad runs and the prior probabilities of which runs being bad, we then employ standard Bayesian inference procedures to derive the appropriate analysis. In particular, we consider the linear model and assume that a good…

摘要暂缺

双随机成分线性模型的贝叶斯分析：平衡不完全区组设计AI 解读

Abstract : The paper considers linear models which contain two random components and enable representation of general designs with block effects random. It shows how, within a Bayesian framework, to combine the intra- and inter-block information about the location parameters. The balanced incomplete block design, an important special case, is considered in detail and illustrated by an example. A simple approximation to the marginal distribution of an individual contrast is…

若干异常值问题的贝叶斯处理AI 解读

The problem of outlying observations is considered from a Bayesian viewpoint. We suppose that each of the observations in an experiment in an experiment may come from either a ‘good’ run or a ‘bad’ run. By specifying the models corresponding to good and bad runs and the prior probabilities of which runs being bad, we then employ standard Bayesian inference procedures to derive the appropriate analysis. In particular, we consider the linear model and assume that a good…