We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the (m k) possible choices of sensor measurements is not practical unless m and k are small. In this paper, we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m 3 operations; for m= 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2-GHz personal computer.

/pdf/sensor-selection-via-convex-optimization-2nryt3q469.pdf

Sensor Selection via Convex Optimization

It is a major goal of clinical pharmacology to understand the dose-effect relationship in therapeutics. Much progress towards this goal has been made in the last 2 decades through the development of pharmacokinetics as a discipline. The study of pharmacokinetics seeks to explain the time course of drug concentration in the body. Recognition of the crucial concepts of clearance and volume of distribution has provided an important link to the physiological determinants of drug disposition. Mathematical models of absorption, distribution, metabolism and elimination have been extensively applied, and generally their predictions agree remarkably well with actual observations. However, the time course of drug concentration cannot in itself predict the time course or magnitude of drug effect. When drug concentrations at the effect site have reached equilibrium and the response is constant, the concentration-effect relationship is known as pharmacodynamics. Mathematical models of pharmacodynamics have been used widely by pharmacologists to describe drug effects on isolated tissues. The crucial concepts of pharmacodynamics are potency — reflecting the sensitivity of the organ or tissue to a drug, and efficacy — describing the maximum response. These concepts have been embodied in a simple mathematical expression, the Emax model, which provides a practical tool for predicting drug response analogous to the compartmental model in pharmacokinetics for predicting drug concentration.

Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models.

Objects. The ~ b s t rac t ~ b j ect forms the fundamental base class for the entire design and all other classes are derived from this base class. The Abstract Object class defines and characterizes all the essential properties every class in this 404 OBJECT-ORIENTED SIMULATION

Handbook of Simulation

Model-based design of experiments for parameter precision: State of the art

Response sarfxe methodology (RSM) is a collection of tools developed in the 1950s for the purpose of determining optimum operating conditions in applications in the chemical industry. This article reviews the progrrss of RSM in the general areas of experimental design and analysis and indicates how its role has been affected by advanccs in other fields of applied statistics. Current areas of research in RSM are highlighted. and areas for future research are discussed.

https://www.jstor.org/stable/pdfplus/1268813.pdf

Response surface methodology: 1966–1988

After stating the model and the design problem, we briefly present the results for regression design prior to the work of Kiefer and Wolfowitz. We then review the major results of Kiefer and Wolfowitz, particularly those on the theory of design, as well as the way the criterion has been extended to non-linear models. Finally, we discuss algorithms for constructing D-optimum designs.

R. C. St. John

Papers

D-Optimality for Regression Designs: A Review