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A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard statistical topics. Nearly all topics are covered in their multivariate setting.The book is intended as a first year graduate course in large sample theory for statisticians. It has been used by graduate students in statistics, biostatistics, mathematics, and related fields. Throughout the book there are many examples and exercises with solutions. It is an ideal text for self study. [Publisher]

519.243 --- Asymptotic distribution (Probability theory) --- Law of large numbers --- Sampling (Statistics) --- Random sampling --- Statistics of sampling --- Statistics --- Mathematical statistics --- Large numbers, Law of --- Numbers, Large --- Convergence --- Probabilities --- Asymptotic expansions --- Central limit theorem --- Distribution (Probability theory) --- 519.243 Sampling. Sampling theory --- Sampling. Sampling theory --- Asymptotic distribution (Probability theory). --- Law of large numbers. --- Sampling (Statistics). --- Echantillonnage (Statistique) --- Échantillonnage (statistique) --- Distribution asymptotique (théorie des probabilités) --- Loi des grands nombres.