Given a parameter of interest, such as a population mean μ or population proportion p, the objective of point estimation is to use a sample to compute a number that represents in some sense a good guess for the true value of the parameter. The resulting number is called a point estimate. In Section 7.1, we present some general concepts of point estimation. In Section 7.2, we describe and illustrate two important methods for obtaining point estimates: the method of moments and the method of maximum likelihood.
- DeGroot, Morris, and Mark Schervish, Probability and Statistics (3rd ed.), Addison-Wesley, Boston, MA, 2002. Includes an excellent discussion of both general properties and methods of point estimation; of particular interest are examples showing how general principles and methods can yield unsatisfactory estimators in particular situations.Google Scholar
- Hoaglin, David, Frederick Mosteller, and John Tukey, Understanding Robust and Exploratory Data Analysis, Wiley, New York, 1983. Contains several good chapters on robust point estimation, including one on M-estimation.Google Scholar
- Hogg, Robert, Allen Craig, and Joseph McKean, Introduction to Mathematical Statistics (6th ed.), Prentice Hall, Englewood Cliffs, NJ, 2005. A good discussion of unbiasedness.Google Scholar
- Larsen, Richard, and Morris Marx, Introduction to Mathematical Statistics (4th ed.), Prentice Hall, Englewood Cliffs, NJ, 2005. A very good discussion of point estimation from a slightly more mathematical perspective than the present text.Google Scholar
- Rice, John, Mathematical Statistics and Data Analysis (3rd ed.), Duxbury Press, Belmont, CA, 2007. A nice blending of statistical theory and data.Google Scholar