Information Technology Reference
In-Depth Information
Chapter 4
Estimation
A
CCURATE, RELIABLE ESTIMATES ARE ESSENTIAL TO EFFECTIVE DECISION-
MAKING.
In this chapter, we review preventive measures and list the pro-
perties to look for in an estimation method. Several robust semiparametric
estimators are considered along with one method of interval estimation,
the bootstrap.
PREVENTION
The vast majority of errors in estimation stem from a failure to measure
what one wanted to measure or what one thought one was measuring.
Misleading definitions, inaccurate measurements, errors in recording and
transcription, and confounding variables plague results.
To forestall such errors, review your data collection protocols and pro-
cedure manuals before you begin, run several preliminary trials, record
potential confounding variables, monitor data collection, and review the
data as they are collected.
DESIRABLE AND NOT-SO-DESIRABLE ESTIMATORS
“The method of maximum likelihood is, by far, the most popular tech-
nique for deriving estimators” Casella and Berger [1990, p. 289]. The
proper starting point for the selection of the “best” method of estimation
is with the objectives of our study: What is the purpose of our estimate?
If our estimate is q* and the actual value of the unknown parameter is q,
what losses will we be subject to? It is difficult to understand the popular-
