论文检索

固定区间平滑算法的几何推导AI 解读

The Kalman filter fixed-interval smoothing algorithm is derived from geometrical considerations. This approach is considerably simpler than standard methods.

相关数据的位置参数估计量AI 解读

Asymptotic properties of an estimator of location, Jn, which is an asymptotically best linear unbiased estimator for the location parameter of the logistic distribution, are studied for dependent data. It is shown that Jn is asymptotically normally distributed for strongly-mixing dependent data. The asymptotic variance of Jn is obtained in general and computed for strictly stationary first-order Gaussian autoregressive processes. The performance of Jn for dependent data is…

自回归模型的 Cramér–Rao 下界与稳健 M 估计AI 解读

A Cramér-Rao lower bound is computed for estimates of the location, innovations scale and autoregressive parameters for a finite-variance pth-order autoregression. The implication of the bound is that the usual least-squares estimates of all of these parameters have asymptotically the same lack of efficieney robustness toward heavy-tailed innovations distributions as does the sample mean for estimating location. On the other hand, autoregression analogues of Huber's…

高斯移动平均过程参数的最大似然估计AI 解读

Several authors have described methods for computing the exact maximum likelihood estimates for the parameters of a Gaussian moving average process. In this paper, an iterative procedure for solving the likelihood equations is derived which includes the covariance determinant. The procedure expresses the estimator of each parameter as a linear combination of a suitably large set of sample serial correlations which has many of the computational advantages of the direct…

多维格点上平稳过程的参数估计AI 解读

We study asymptotic properties of various estimation procedures for a general stationary regular process on a d-dimensional lattice. Differences between d = 1, time series, and d ≥ 2 spatial processes, are pointed out. We suppose that the process is observed on a set PN, with cN points, which tends to infinity with the same speed in all directions. The relative edge effect is of order cN−1/d, increasing with d: this effect is without statistical importance if d = 1, but is…

不连续决策过程与门限自回归时间序列建模AI 解读

Setting the problem of approximating an underlying nonlinear time series model within the framework of Bayesian decision theory, we demonstrate how the general analysis of discontinuous decision processes developed by Smith, Harrison & Zeeman (1981) leads naturally to a threshold autoregression.

四阶累积量谱密度积分的估计AI 解读

It is known that the asymptotic variance of a quasimaximum likelihood estimate for a non-Gaussian process contains certain integrals of the fourth order cumulant spectral density. If we apply the asymptotic theory we are required to estimate these integrals. Here we shall propose some operational consistent estimates for them.

存在缺失观测的平稳高斯 ARMA 过程似然函数AI 解读

The likelihood function for an autoregressive-moving average process observed at n equally spaced time points has a well-known form. This note gives an expression for the likelihood function when some observations are missing from the series and shows how the missing observations may be estimated from the available data.

用于多元时间序列建模的折扣多项式AI 解读

It is proposed to adopt Schmidt's (1974) modified polynomial lag models for multiple time series analysis. The resulting method can be regarded as a compromise between modelling pure autoregressive and autoregressive-moving average processes. While being as easy to specify and estimate as the former, the resulting class of models avoids some disadvantages that result from fitting pure autoregressive structures. As an example, data of Quenouille (1957) are analysed.

都市工资率的单因子多元时间序列模型AI 解读

The paper formulates and estimates a single-factor multivariate time series model. The model is a dynamic generalization of the multiple indicator (or factor analysis) model. It is shown to be a special case of the general state space model and can be estimated by maximum likelihood methods using the Kalman filter algorithm. The model is used to obtain estimates of the unobserved metropolitan wage rate for Los Angeles, based on observations of sectoral wages within the…

时间自相关下设计的稳健性（二）：一阶自回归过程的最优性与数值结果AI 解读

The optimum design and its efficiency relative to the uniform design for estimating the mean of a stationary First-order autoregressive process plus an independent error are characterized completely. This is related to the work of Jones (1948), Optimum designs and variances of least squares estimates are calculated numerically for the problem of estimating the slope in a simple linear regression when the errors follow the above structure, for a range of values of the sample…

混合线性模型的最佳线性递归估计AI 解读

Recursive estimation techniques for fixed and completely random models are extended to mixed linear models. The Kalman filter is used to obtain recursive estimators for a two-part random model where the second random factor obeys a generalized autoregressive process. By passing to the limit in an appropriate way, recursions for the mixed model are derived.

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.

利用岭回归估计营销中的直接滞后效应AI 解读

A prediction criterion for ridge regression is developed and used for estimating lagged effect patterns by means of the direct lag model. As examples, the Lydia Pinkham monthly and annual data are analyzed, showing that lag patterns can be obtained that are in agreement across time intervals.

计量经济模型中事后预测方差的一则札记AI 解读

IT IS WELL KNOWN that in an econometric model, under general assumptions, the ex-post forecast error (conditional on predetermined variables) can be decomposed into the sum of two independent terms; the former is the component due to error in estimated coefficients, while the latter depends on the random error terms. The asymptotic covariance matrix of the first component can be analytically derived through intermediate computation of the covariance matrix of restricted…

论贝叶斯稳态预测模型AI 解读

Summary A recent criterion of Smith (1979) seeks to generalize the steady forecasting model of Harrison and Stevens (1976) to take account of non-normality in the observation and system variables. In this note, predictive distributions and forecasts corresponding to quadratic and step loss functions are examined when the observation variable has a distribution belonging to the exponential family. It is shown that Smith's generalized model does not always yield forecasts…

某类谱密度模型的参数估计程序AI 解读

Summary Let {X(t)} be a Gaussian stationary process with mean zero and true spectral density g(x). We shall fit a certain spectral model f θ (x) for this process, and propose an estimate of θ, say θ^n, by minimizing a criterion D{f θ , g), which measures the nearness of f θ (x) to g(x). Also we shall derive the asymptotic distribution of θ^n. For the case of some appropriate spectral parametrization of f θ (x) we show that θ^n can be expressed in an explicit form.…

样本时空自相关函数的方差AI 解读

Summary An important part of the diagnostic checking of space-time autoregressive moving average (starma) models is testing the temporal independence of the residuals. In the context of the three-stage modelling procedure, such a test is based on the sample space-time autocorrelation function. This paper developes approximate variances of the sample space-time autocorrelation function when the underlying process is white noise, which are needed to test significance of the…

随机游走模型参数最小二乘估计量极限分布的计算AI 解读

The limit distribution of the least squares estimator $\\hat{\\alpha}$ of the parameter $\\alpha$ of the first order stochastic difference equation, in the boundary case $\\alpha = 1$, is calculated.

线性时间序列模型检验的一种方法AI 解读

The purpose of this paper is to develop diagnostic tests for open-loop transfer function models with autoregressive-moving average stochastic disturbances using the efficient scores procedure. The tests proposed are asymptotically equivalent to those based upon the likelihood ratio principle and have the advantage that they do not involve a heavy computational burden. Consideration is given both to the standard case of nonsingular information matrices and to the situation…

未知自回归阶数对预测均方误差的影响（一）AI 解读

Given a realization of T consecutive observations of an autoregressive process, x, of unknown order m, a process of an arbitrary order p is assumed to be fitted for predicting the future values of another process [xtilde](t), where [xtilde](t) is independent of xt but otherwise has identical probabilistic structure. The mean squared error of prediction when the autoregressive coefficients are estimated by least squares is obtained to terms of order T -1. The bias in…

大量时间序列的预测AI 解读

Principles for constructing estimators of the Stein type (shrinkage estimators) are discussed in general terms, with emphasis on underlying assumptions. The problem of parameter estimation and prediction for multiple time series is examined with these principles in mind, particularly for the case in which the number of time series is large and the number of observations from each series is small. Our results are applied to the problem of demand estimation in an inventory…

S 阵列与 Box–Jenkins ARMA 模型识别方法的关系AI 解读

This paper investigates an extension of the partial autocorrelation function, which we call the generalized partial autocorrelation function. These generalized partial autocorrelations, which are not true correlations except when p = 0, are useful in examining the relationship between the S array method of Gray, Kelley, and McIntire (1978) and the Box-Jenkins approach to ARMA model identification. Also, the generalized partial autocorrelation is shown to be a useful model…

预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…