The estimation of continuous-time systems using discrete data

Continuous-time AR process parameter estimation from discrete-time data

The problem of estimating continuous-time autoregressive process parameters from discrete-time data is considered. The basic approach used here is based on replacing the derivatives in the model by discrete-time di erences, forming a linear regression and using the least squares method. It is known, however, that all standard approximations of the highest order derivative give a biased least sq...

Optimal Finite-time Control of Positive Linear Discrete-time Systems

This paper considers solving optimization problem for linear discrete time systems such that closed-loop discrete-time system is positive (i.e., all of its state variables have non-negative values) and also finite-time stable. For this purpose, by considering a quadratic cost function, an optimal controller is designed such that in addition to minimizing the cost function, the positivity proper...

The Relationship Between Discrete Time and Continuous Time Linear Estimation

We examine the problem of discrete time system estimation while not ignoring the underlying continuous time system. This leads to the use of a new discrete time operator , the operator, which approximates the continuous time derivative operator d dt. We use this to formulate system estimation algorithms, and discuss their signiicantly superior numerical properties when compared to the equivalen...

Discrete-time Equivalents to Continuous-time Systems

Indirect estimation of a discrete-state discrete-time model using secondary data analysis of regression data.

Multi-state models of chronic disease are becoming increasingly important in medical research to describe the progression of complicated diseases. However, studies seldom observe health outcomes over long time periods. Therefore, current clinical research focuses on the secondary data analysis of the published literature to estimate a single transition probability within the entire model. Unfor...