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删失且自相关数据的回归分析AI 解读

For many studies in which data are collected sequentially in time, the sensitivity of the measurement is limited and an exact value can be recorded only if it falls within a specified range. This gives rise to a censored time series. In this article, we present a methodology for regression analysis of censored time series data. We fit autoregressive models to account for the time dependence. Two numerical methods for full likelihood estimation and an approximate method are…

缺失数据下时空 ARMAX 模型的估计与识别AI 解读

A method for modeling and fitting multivariate spatial time series data based on current spatial methodology coupled with the parameterization of the ARMAX model is presented. Because of the physical constraints imposed on multivariate data collection in both space and time, the estimation and identification procedures tolerate general patterns of missing or incomplete data.

含缺失观测的 ARIMA 模型：估计、预测与插值AI 解读

We show how to define and then compute efficiently the marginal likelihood of an ARIMA model with missing observations. The computation is carried out by using the univariate version of the modified Kalman filter introduced by Ansley and Kohn (1985a), which allows a partially diffuse initial state vector. We also show how to predict and interpolate missing observations and obtain the mean squared error of the estimate.

ARIMA 信号提取中的修订AI 解读

The problem of decomposing an observed series, assumed to follow an ARIMA process, into signal plus noise is considered. It is well known that the preliminary estimates of the signal will be subject to revisions as more data become available. For a general ARIMA process, the revision in the concurrent estimate of the signal is seen to follow a stationary ARMA process, easily derived from the overall series model. The results are extended to non-concurrent preliminary…

ARIMA 信号提取中的修订AI 解读

The problem of decomposing an observed series, assumed to follow an ARIMA process, into signal plus noise is considered. It is well known that the preliminary estimates of the signal will be subject to revisions as more data become available. For a general ARIMA process, the revision in the concurrent estimate of the signal is seen to follow a stationary ARMA process, easily derived from the overall series model. The results are extended to non-concurrent preliminary…

缺失数据下时空 ARMAX 模型的估计与识别AI 解读

A method for modeling and fitting multivariate spatial time series data based on current spatial methodology coupled with the parameterization of the ARMAX model is presented. Because of the physical constraints imposed on multivariate data collection in both space and time, the estimation and identification procedures tolerate general patterns of missing or incomplete data.

非高斯平稳时间序列的独立同分布创新移动平均表示唯一性AI 解读

Full-spectrum, non-Gaussian stationary series can have at most one representation as a moving average of independent and identically distributed random variables having finite moments of all orders.

异方差一阶自回归模型的得分检验AI 解读

A score test is proposed for simultaneous testing of independence and homoscedasticity in the first-order autoregressive model with nonconstant variance. The relationships between the score test statistic and the local influence of minor perturbations on a statistical model are examined.

季节性移动平均模型的快速滤波AI 解读

Pearlman (1980) gives a fast filtering algorithm for an ARMA, i.e. autoregressive-moving average, model. When the algorithm is applied to a seasonal moving average model significant computational savings can be obtained by taking advantage of the structural zeros noted by Kohn & Ansley (1984) and Melard (1984). In this paper we identify a second set of structural zeros which leads to further significant computational savings. Our results can be applied to produce a fast…

若干序列结构协方差矩阵的分解AI 解读

Serially-structured variance matrices which can be decomposed into lower-triangular band matrices are defined. These may be thought of as the variance matrices of finite realizations of generalized autoregressive-moving average processes. A large number of serially-generated stochastic processes are shown to be of this type.

含一阶单整过程的多元时间序列回归AI 解读

This paper develops a general asymptotic theory of regression for processes which are integrated of order one. The theory includes vector autoregressions and multivariate regressions amongst integrated processes that are driven by innovation sequences which allow for a wide class of weak dependence and heterogeneity. The models studied cover cointegrated systems such as those advanced recently by Granger and Engle and quite general linear simultaneous equations systems with…

允许非连续观测的回归—ARMA时间序列模型精确极大似然估计AI 解读

SUMMARY An explicit procedure is given to obtain the exact maximum likelihood estimates of the parameters in a regression model with ARMA time series errors with possibly nonconsecutive data. The method is based on an innovation transformation approach from which an explicit recursive procedure is derived for the efficient calculation of the exact likelihood function and associated derivatives. The innovations and associated derivatives are used to develop a modified…

观测时间序列单位根检验的理论AI 解读

This paper provides a framework for testing for a unit root in an observed time series against some alternatives considered previously by Anderson (1948). Some new tests for the unit root null hypothesis for the errors affecting a classical regression model against the non-stationary (including explosive) alternative hypothesis are developed. The previous results of Sargan and Bhargava (1983) and the new test statistics are then applied to test the simple random walk and the…

系统性缺失观测下向量 ARMA 过程的预测AI 解读

The following two predictors are compared for time series with systematically missing observations: (a) A time series model is fitted to the full series Xt , and forecasts are based on this model, (b) A time series model is fitted to the series with systematically missing observations Y τ, and forecasts are based on the resulting model. If the data generation processes are known vector autoregressive moving average (ARMA) processes, the first predictor is at least as…

广义最小二乘估计量风险上界的一种方法AI 解读

First, an approach to an upper bound for the risk matrix of GLSE's is established when the information on the parameter space of the structural parameter in the covariance matrix of the error can be utilized. Second, this result is applied to regression with (i) serial correlation and (ii) heteroscedastic covariance structure. In the heteroscedastic regression, the problem of estimating the common mean of two normal populations is studied in detail.

移动平均过程样本协方差与相关函数的极限理论AI 解读

Let $X_t = \sum^\infty_{j=-\infty} c_jZ_{t-j}$ be a moving average process where the $Z_t$'s are iid and have regularly varying tail probabilities with index $\alpha > 0$. The limit distribution of the sample covariance function is derived in the case that the process has a finite variance but an infinite fourth moment. Furthermore, in the infinite variance case $(0 < \alpha < 2)$, the sample correlation function is shown to converge in distribution to the ratio of two…

单变量时间序列模型识别的贝叶斯程序AI 解读

This paper is concerned with model selection in time series analysis. An identification criterion is presented that is asymptotically equivalent to a Bayes decision rule. The discussion is conducted in the context of a general class of parametric time series models and consideration is given to the special case of order determination in autoregressive moving-average representations. Consistency of the criterion is proved.

强依赖平稳高斯时间序列参数估计的大样本性质AI 解读

A strongly dependent Gaussian sequence has a spectral density $f(x, \theta)$ satisfying $f(x, \theta) \sim |x|^{-\alpha(\theta)} L_\theta(x)$ as $x \rightarrow 0$, where $0 < \alpha(\theta) < 1$ and $L_\theta(x)$ varies slowly at 0. Here $\theta$ is a vector of unknown parameters. An estimator for $\theta$ is proposed and shown to be consistent and asymptotically normal under appropriate conditions. These conditions are satisfied by fractional Gaussian noise and fractional…

时间序列确定性线性趋势的检验AI 解读

Most powerful tests are derived for the hypothesis that deterministic linear trend occurs in time series against the alternative that trend consists of random walk subject to drift. Comparisons of these tests and of the tests suggested by LaMotte and McWhorter (1978) are made in terms of exact powers and Pitman asymptotic relative efficiencies.

利用互相关模式检验两条时间序列的独立性AI 解读

The Haugh (1976) test for independence employs the univariate residual cross-correlation function. However, it ignores information about a possible pattern in successive cross-correlation coefficients. An asymptotic test is developed that incorporates this information and includes the Haugh test as a special case. A Monte Carlo study indicates that the proposed test is more powerful than the Haugh s and regression F tests for certain models. Two empirical examples are…

天气与售电量关系的半参数估计AI 解读

A nonlinear relationship between electricity sales and temperature is estimated using a semiparametric regression procedure that easily allows linear transformations of the data. This accommodates introduction of covariates, timing adjustments due to the actual billing schedules, and serial correlation. The procedure is an extension of smoothing splines with the smoothness parameter estimated from minimization of the generalized cross-validation criterion introduced by…

利用交叉相关函数潜在模式检验两条时间序列的独立性AI 解读

The Haugh (1976) test for independence employs the univariate residual cross-correlation function. However, it ignores information about a possible pattern in successive cross-correlation coefficients. An asymptotic test is developed that incorporates this information and includes the Haugh test as a special case. A Monte Carlo study indicates that the proposed test is more powerful than the Haugh s and regression F tests for certain models. Two empirical examples are…

线性动态模型中乘数的置信域AI 解读

On donne des regions de confiance pour des multiplicateurs qui reconnaissent la variabilite d'echantillonnage des chocs estimes

高阶穿越的渐近方差及其在快速白噪声检验中的应用AI 解读

The rate of decrease in the variance of higher order crossings is obtained for stationary m-dependent Gaussian sequences. This result provides a quick approximation to the variance of the number of axis-crossings of m-dependent processes, and is applied in determining approximate probability limits for the higher order crossings of white noise. The limits are used in testing for white noise in simulated and also real data examples.