Accession Number:

ADA459332

Title:

Multiscale Smoothing Error Models

Descriptive Note:

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS

Personal Author(s):

• Luettgen, Mark R.
• Willsky, Alan S.

Report Date:

1994-03-01

Pagination or Media Count:

12.0

Abstract:

A class of multiscale stochastic models based on scale-recursive dynamics on trees has recently been introduced. These models are interesting because they can be used to represent a broad class of physical phenomena and because they lead to efficient algorithms for estimation and likelihood calculation. In this paper, we provide a complete statistical characterization of the error associated with smoothed estimates of the multiscale stochastic processes described by these models. In particular, we show that the smoothing error is itself a multiscale stochastic process with parameters which can be explicitly calculated.

Descriptors:

• *STOCHASTIC PROCESSES
• ALGORITHMS
• MAXIMUM LIKELIHOOD ESTIMATION
• EFFICIENCY
• ESTIMATES
• ERRORS
• RECURSIVE FUNCTIONS

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE