A First Course in Order StatisticsWritten in a simple style that requires no advanced mathematical or statistical background, A First Course in Order Statistics introduces the general theory of order statistics and their applications. The book covers topics such as distribution theory for order statistics from continuous and discrete populations, moment relations, bounds and approximations, order statistics in statistical inference and characterization results, and basic asymptotic theory. There is also a short introduction to record values and related statistics. The authors have updated the text with suggestions for further reading that readers may use for self-study. Audience: advanced undergraduate and graduate students in statistics and mathematics, practicing statisticians, engineers, climatologists, economists, and biologists. |
Other editions - View all
A First Course in Order Statistics Barry C. Arnold,N. Balakrishnan,H. N. Nagaraja Limited preview - 2008 |
A First Course in Order Statistics Barry C. Arnold,N. Balakrishnan,H. N. Nagaraja No preview available - 1992 |
Common terms and phrases
absolutely continuous assume asymptotically normal b₁ Balakrishnan binomial BLUE cdf F cdf F(x censored sample common distribution computation convergence covariances denote derive determine discussed distribution F distribution function equation example Exercise expected value exponential distribution expression finite geometric distribution given Hence Hi:n independent integral ith order statistic joint density function joint pmf limit distribution linear location parameter logistic distribution Markov chain Math moments of order MVUE Nagaraja normal distribution norming constants Note observations obtain order statistics outliers P₁ P₂ population with pdf product moments Q-Q plot quantile quantile function random sample random variables record value recurrence relations sample maximum sample median scale parameters Section sequence standard exponential distribution standard normal sufficient statistic Suppose symmetric Theorem tion truncated uniform distribution Uniform(0 variance W₁ X₁ X₁:n Xi:n Xj:n Y₁ Y₁:n Y₂ Σ Σ