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时间序列过去与未来的典型相关：定义与理论AI 解读

The concepts of canonical correlations and canonical components are familiar ideas in multivariate statistics. In this paper we extend these notions to stationary time series with a view to determining the most predictable aspect of the future of a time series. We relate properties of the canonical description of a time series to well known structural properties of the series such as (i) rational spectra (i.e., ARMA series), (ii) strong mixing, (iii) absolute regularity, etc.

获得方差参数一致估计量的困难AI 解读

This paper considers the estimation of variances by pooling information from a large number of small samples, when the means are nuisance parameters about which no assumptions or prior knowledge are available. When the variance is proportional to the square of the mean we obtain a consistent estimator from a marginal likelihood. However, this method cannot be generalized to other variance functions. Integrated likelihood, modified likelihood and partial conditional…

获得方差参数一致估计量的困难AI 解读

This paper considers the estimation of variances by pooling information from a large number of small samples, when the means are nuisance parameters about which no assumptions or prior knowledge are available. When the variance is proportional to the square of the mean we obtain a consistent estimator from a marginal likelihood. However, this method cannot be generalized to other variance functions. Integrated likelihood, modified likelihood and partial conditional…