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传染病传播模型中接触率的最小二乘估计AI 解读

The contact rate and associated contact number determine the rate of spread of an infectious disease whose population dynamics are described by a mathematical model. It is shown how to obtain least-squares estimates of the contact rate for a standard homogeneous univariate model and a four-group model with different contact rates between the groups. The results of a small scale simulation are presented in the case of the four-group model. It is argued that the least-squares…

传染病传播模型中接触率的最小二乘估计AI 解读

The contact rate and associated contact number determine the rate of spread of an infectious disease whose population dynamics are described by a mathematical model. It is shown how to obtain least-squares estimates of the contact rate for a standard homogeneous univariate model and a four-group model with different contact rates between the groups. The results of a small scale simulation are presented in the case of the four-group model. It is argued that the least-squares…

相依回归量下的稳健回归估计：函数型最小二乘方法AI 解读

In the case of regression models, one robust estimation procedure which has recently emerged is that of functional least squares. The procedure is based on the use of characteristic functions for which the tail behavior is relected by the behavior of these functions at the origin. Its attraction is that it is applicable to situations where the distribution of the disturbances may be long-tailed and/or asymmetric. This paper extends this theory to include a large class of…

随机系数自回归模型导论AI 解读

1 Introduction.- 1.1 Introduction.- Appendix 1.1.- Appendix 1.2.- 2 Stationarity and Stability.- 2.1 Introduction.- 2.2 Singly-Infinite Stationarity.- 2.3 Doubly-Infinite Stationarity.- 2.4 The Case of a Unit Eigenvalue.- 2.5 Stability of RCA Models.- 2.6 Strict Stationarity 37 Appendix 2.1.- 3 Least Squares Estimation of Scalar Models.- 3.1 Introduction.- 3.2 The Estimation Procedure.- 3.3 Strong Consistency and the Central Limit Theorem.- 3.4 The Consistent Estimation of…

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多元自回归移动平均模型的精确似然函数AI 解读

By making use of the properties of tensor products, this paper describes the derivation of an expression for the exact likelihood function of a stationary process generated by a vector autoregressive-moving average model using concentrated maximum likelihood techniques. Furthermore, in the process of deriving the likelihood function, a closed form expression for the covariance function of the process in terms of the coefficients of the model is derived.

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预测误差方差的估计AI 解读

Spectral methods are used to construct an estimate of the variance of the prediction error for a normal, stationary process. The estimate obtained is shown to be strongly consistent and asymptotically normally distributed. Some aspects of the computations with respect to the fast Fourier transform are considered. The latter half of the article consists of a number of simulations, based on both generated and real data, which illustrate the results obtained. The relation…

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向量线性时间序列模型估计方法的比较AI 解读

Akaike (1973) showed, in the case of scalar autoregressive-moving average models, that the estimates obtained by an application of the Newton-Raphson algorithm to the approximate likelihood function are the same as those obtained by Hannan (1969). By making use of the properties of tensor products, this paper extends these ideas to show that, in the case of vector linear time series models, the estimates obtained by the application of the Newton-Raphson procedure are…

向量线性时间序列模型的有效估计AI 解读

A procedure is given to estimate efficiently the coefficients of a general class of vector linear time series models. The procedure is based on spectral techniques. The estimated model is shown to contain, as particular cases, many of the time series models which have appeared in the literature.

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