All of electromagnetics, whether measurement, analysis, or computation, may be regarded as activities of information acquisition, processing, and presentation. It is relevant to mention the information-intensive nature of electromagnetics, because this property can provide a useful, unifying perspective from which might be developed efficiency improvements in these various areas. If, for example, redundant information can be reduced or avoided in numerical calculations, the result should be a possibly substantial decrease in the amount of computer time that is needed for computer modeling. In this paper, we consider the use of a generalized signal-processing approach called “model-based parameter estimation” (MBPE) for making various improvements in either the efficiency of electromagnetic computer modeling or the efficiency of representing electromagnetic observables. One is to increase the efficiency of the basic model computation itself, a typical example being that of replacing the rigorous Green's function for the antenna-interface problem by a simpler, more easily computable, approximation. Another is to make more efficient use of the results that are computed, for example, by reducing the number of samples needed to construct a transfer function over some frequency band. A third arises because the electromagnetic fields/sources in various transformed-pair domains, such as exhibited by the far fields and locations of a linear-source array, are described by exponential and pole series from which physically relevant parameters can be extracted, which are useful for such purposes as data compression and physical interpretation. The discussion below deals primarily with MBPE based on exponential- and poles-series models which yield generalized waveform- and spectral-domain response functions of various transform-pair variables. We demonstrate how sampling such observables in terms of the appropriate variable or derivatives thereof leads to a data matrix from which the model parameters can be computed. Various kinds of data and models are used to illustrate the breadth of potential applications, with an emphasis on estimating frequency responses from frequency-sampled data. Some concluding remarks are addressed to use of MBPE for the Green's-function application.