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Point Estimation Theory

In: Mathematical Statistics for Economics and Business

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  • Ron C. Mittelhammer

    (Washington State University, School of Economic Sciences)

Abstract

The problem of point estimation examined in this chapter is concerned with the estimation of the values of unknown parameters, or functions of parameters, that represent characteristics of interest relating to a probability model of some collection of economic, sociological, biological, or physical experiments. The outcomes generated by the collection of experiments are assumed to be outcomes of a random sample with some joint probability density function $$ f({{x}_{1}},\ldots, {{x}_n};\vec{\Theta} ) $$ . The random sample need not be from a population distribution, so that it is not necessary that X 1,…,X n be iid. The estimation concepts we will examine in this chapter can be applied to the case of general random sampling, as well as simple random sampling and random sampling with replacement, i.e., all of the random sampling types discussed in Chapter 6. The objective of point estimation will be to utilize functions of the random sample outcome to generate good (in some sense) estimates of the unknown characteristics of interest.

Suggested Citation

  • Ron C. Mittelhammer, 2013. "Point Estimation Theory," Springer Books, in: Mathematical Statistics for Economics and Business, edition 2, chapter 7, pages 363-431, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-5022-1_7
    DOI: 10.1007/978-1-4614-5022-1_7
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