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  • Sách/Book

  • Elementary statistics : a step by step approach

    • Authors: Allan G. Bluman (2018)

  • This text is aimed at students who do not have a mathematical background. It therefore uses a non-theoretical approach, and concepts are explained intuitively, without the use of formal proofs; they are instead supported by example.
  • Sách/Book

  • Theory of statistical inference

    • Authors: Anthony Almudevar. (2021)

  • "The purpose of applying mathematical theory to the theory of statistical inference is to make it simpler and more elegant. Theory of Statistical Inference is concerned with the development of a type of optimization theory which can be used to inform the choice of statistical methodology. Of course, this would be pointless without reference to such methods. We are simply noting that they are included in support of the larger goal. This book distinguishes itself from other graduate textbooks because it is written from the point of view that some degree of understanding of measure theory, as well as other branches of mathematics, which include topology, group theory and complex analysis, should be a part of the canon of statistical inference
  • Sách/Book

  • Introduction to Mathematical Statistics

    • Authors: Robert V. Hogg (2006)

  • This classic book retains its outstanding ongoing features and continues to provide readers with excellent background material necessary for a successful understanding of mathematical statistics. Chapter topics cover classical statistical inference procedures in estimation and testing, and an in-depth treatment of sufficiency and testing theory—including uniformly most powerful tests and likelihood ratios. Many illustrative examples and exercises enhance the presentation of material throughout the book. For a more complete understanding of mathematical statistics.
  • Sách/Book

  • All of nonparametric statistics

    • Authors: Larry Wasserman (2006)

  • This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory
  • Sách/Book

  • Statistical inference

    • Authors: George Casella (1990)

  • This text provides information on topics such as ancillarity, invariance, Bayesian methods, pivots, Stein estimation, errors in variables and inequalities. The authors discuss both theoretical statistics and the practical applications of the theoretical developments. Many ideas are introduced in the context of data analysis rather than pure mathematics