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Central limit theorem

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Jason Gibson gives an in-depth explanation of the central limit theorem. He breaks down each aspect of the theorem into chunks that students can understand.

Central limit theorem.

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This book shows how the central limit theorem for independent, identically distributed random variables with values in general, multidimensional spaces, holds uniformly over some large classes of functions. The author, an acknowledged expert, gives a thorough treatment of the subject, including several topics not found in any previous book, such as the Fernique-Talagrand majorizing measure theorem for Gaussian processes, an extended treatment of Vapnik-Chervonenkis combinatorics, the Ossiander L2 bracketing central limit theorem, the Giné-Zinn bootstrap central limit theorem in probability, the Bronstein theorem on approximation of convex sets, and the Shor theorem on rates of convergence over lower layers. Other results of Talagrand and others are surveyed without proofs in separate sections. Problems are included at the end of each chapter so the book can be used as an advanced text. The book will interest mathematicians working in probability, mathematical statisticians and computer scientists working in computer learning theory.

Central limit theorem.

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Jason Gibson provides example problems on applying the central limit theorem to the population mean.

Population research. --- Population --- Central limit theorem. --- Statistical methods.

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Jason Gibson explains how to apply the central limit theorem to population proportions. He also covers how to use a sample proportion. Gibson then provides example problems for the central limit theorem as applied to population proportions.

Population research. --- Population --- Central limit theorem. --- Statistical methods.