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Statistical Matters

In: Multilateral Wellbeing Comparison in a Many Dimensioned World

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  • Gordon Anderson

    (University of Toronto)

Abstract

This chapter presents a hopefully concise summary of the nature and properties of, and relationships between, statistical concepts that are used in later analysis. It is not intended as a rigorous statistical discourse on such matters; for that the reader should consult the more detailed and thorough analyses in Linton (Probability, Statistics and Econometrics. London: Academic Press, 2017), McLachlan and Peel (Finite Mixture Models. New York: Wiley, 2000), Poirier (Intermediate Statistics and Econometrics: A Comparative Approach. Cambridge, MA: MIT Press, 1995), Rao (Linear Statistical Inference and Its Applications. New York: Wiley, 1973) and Whang (Econometric Analysis of Stochastic Dominance: Concepts, Methods, Tools, and Applications. Cambridge: Cambridge University Press, 2019, Silverman (Density Estimation for Statistics and Data Analysis, London Chapman and Hall 1986)). The properties of probability distributions, the instruments that describe the allocation of an individuals’ characteristic (e.g. their income) across a population are outlined. Particular attention is paid to Kernel estimation techniques, the way probability distribution functions are estimated when there is no information regarding their parametric structure. Finally, the notion of stochastic dominance, a comparison technique for comparing distributions is discussed.

Suggested Citation

  • Gordon Anderson, 2019. "Statistical Matters," Global Perspectives on Wealth and Distribution, in: Multilateral Wellbeing Comparison in a Many Dimensioned World, chapter 0, pages 23-60, Palgrave Macmillan.
    • Handle: RePEc:pal:gpochp:978-3-030-21130-1_2
    DOI: 10.1007/978-3-030-21130-1_2
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