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Univariate Normal Distribution and Its Quantile

In: Decision Processes by Using Bivariate Normal Quantile Pairs

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  • N. C. Das

    (Birsa Agricultural University, Department of Statistics)

Abstract

The theme of this book is about quantile values of probability distribution and their applications in decision-making. This chapter highlights the univariate normal distribution, which is the most explored and exploited one. It, therefore, begins with brief characterization of such probability distribution by Ludeman (random processes, filtering, estimation and detection. Wiley India, New Delhi, 2010). These are followed by a search for definitions of quantile by early writers as well as by some later ones. Their main concern was its applications for uncertain inference in terms of confidence probability and in transforming uncertain optimization problem to its unique chance-constrained programming problem. In fact, its use as Neyman’s confidence interval, as well as critical difference, happened to be of foremost importance in empirical decision-making. The chapter also touches upon the determination of nonparametric univariate quantiles. Those interested only in use of quantile may skip Sect. 3.1.

Suggested Citation

  • N. C. Das, 2015. "Univariate Normal Distribution and Its Quantile," Springer Books, in: Decision Processes by Using Bivariate Normal Quantile Pairs, edition 127, chapter 0, pages 47-59, Springer.
    • Handle: RePEc:spr:sprchp:978-81-322-2364-1_3
    DOI: 10.1007/978-81-322-2364-1_3
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