ON DISCRETE SAMPLING OF TIME-VARYING CONTINUOUS-TIME SYSTEMS
研究由线性随机微分方程生成的多元连续时间过程，该过程仅在离散时间点可观测，推导了观测时间序列的模型，并讨论了等间距观测下的高阶模型及统计推断问题。
We consider a multivariate continuous-time process, generated by a system of linear stochastic differential equations, driven by white noise, and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing significantly simplifies matters). Such settings represent partial extensions of ones studied extensively by A.R. Bergstrom. A model for the observed time series is deduced. Initially we focus on a first-order model, but higher-order models are discussed in the case of equally-spaced observations. Some discussion of issues of statistical inference is included.