Singular functional estimation problems cover an extremely large portion of statistical theory. Some procedures with optimal rates have been well established; but recent work on white noise problems has led to a unified approach based on the modulus of the functional. This approach gives optimal or near optimal procedures quite simply for many important statistical problems. For some statistical estimation problems, optimal methods are well known, but for many more either optimality of the methodology is uncertain or its efficiency is unknown. This research will produce generally efficient procedures which can have many scientific applications, for example complex problems like reconstruction of the "true image" from tomography or other imaging techniques. This unified approach to estimation should find applications in engineering, biology, economics and other fields where practical and theoretical scientific problems are framed in terms of statistical models.