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Testing Statistical Hypotheses, 2e Book

Testing Statistical Hypotheses, 2e
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  • Testing Statistical Hypotheses, 2e
  • Written by author E.L. Lehmann
  • Published by Springer-Verlag New York, LLC, January 1997
  • This classic work, now available from Springer, summarizes developments in the field of hypotheses testing. Optimality considerations continue to provide the organizing principle; however, they are now tempered by a much stronger emphasis on the robustnes
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Book Categories

Authors

    1: The General Decision Problem
    1: Statistical inference and statistical decisions
    2: Specification of a decision problem
    3: Randomization; choice of experiment
    4: Optimum procedures
    5: Invariance and unbiasedness
    6: Bayes and minimax procedures
    7: Maximum likelihood
    8: Complete classes
    9: Sufficient statistics
    10: Problems
    11: References
    2: The Probability Background
    1: Probability and measure
    2: Integration
    3: Statistics and subfields
    4: Conditional expectation and probability
    5: Conditional probability distributions
    6: Characterization of sufficiency
    7: Exponential families
    8: Problems
    9: References
    3: Uniformly Most Powerful Tests
    1: Stating the problem
    2: The Neyman–Pearson fundamental lemma
    3: Distributions with monotone likelihood ratio
    4: Comparison of experiments
    5: Confidence bounds
    6: A generalization of the fundamental lemma
    7: Two‐sided hypotheses
    8: Least favorable distributions
    9: Testing the mean and variance of a normal distribution
    10: Problems
    11: References
    4: Unbiasedness: Theory and First Applications
    1: Unbiasedness for hypothesis testing
    2: One‐parameter exponential families
    3: Similarity and completeness
    4: UMP unbiased tests for multiparameter exponential families
    5: Comparing two Poisson or binomial populations
    6: Testing for independence in a 2 x 2 table
    7: Alternative models for 2 x 2 tables
    8: Some three‐factor contingency tables
    9: The sign test
    10: Problems
    11: References
    5: Unbiasedness: Applications to Normal Distributions; Confidence Intervals
    1: Statistics independent of a sufficient statistic
    2: Testing the parameters of a normal distribution
    3: Comparing the means and variances of two normal distributions
    4: Robustness
    5: Effect of dependence
    6: Confidence intervals and families of tests
    7: Unbiased confidence sets
    8: Regression
    9: Bayesian confidence sets
    10: Permutation tests
    11: Most powerful permutation tests
    12: Randomization as a basis for inference
    13: Permutation tests and randomization
    14: Randomization model and confidence intervals
    15: Testing for independence in a bivariate normal distribution
    16: Problems
    17: References
    6: Invariance
    1: Symmetry and invariance
    2: Maximal invariants
    3: Most powerful invariant tests
    4: Sample inspection by variables
    5: Almost invariance
    6: Unbiasedness and invariance
    7: Admissibility
    8: Rank tests
    9: The two‐sample problem
    10: The hypothesis of symmetry
    11: Equivariant confidence sets
    12: Average smallest equivariant confidence sets
    13: Confidence bands for a distribution function
    14: Problems
    15: References
    7: Linear Hypotheses
    1: A canonical form
    2: Linear hypotheses and least squares
    3: Tests of homogeneity
    4: Multiple comparisons
    5: Two‐way layout: One observation per cell
    6: Two‐way layout; m observations per cell
    7: Regression
    8: Robustness against nonnormality
    9: Scheffe's S‐method: A special case
    10: Scheffe's S‐method for general linear models
    11: Random‐effects model: One‐way classification
    12: Nested classifications
    13: Problems
    14: References
    8: Multivariate Linear Hypothesis
    1: A canonical form
    2: Reduction by invariance
    3: The one‐and two‐sample problems
    4: Multivariate analysis of variance (MANOVA)
    5: Further applications
    6: Simultaneous confidence intervals
    7: x2‐tests: Simple hypothesis and unrestricted alternatives
    8: x2‐ and likelihood‐ratio tests
    9: Problems
    10: References
    9: The Minimax Principle
    1: Tests with guaranteed power
    2: Examples
    3: Comparing two approximate hypotheses
    4: Maximum tests and invariance
    5: The Hunt–Stein theorem
    6: Most stringent tests
    7: Problems
    8: References
    10: Conditional Inference
    1: Mixtures of experiments
    2: Ancillary statistics
    3: Optimal conditional tests
    4: Relevant subsets
    5: Problems
    6: References
    Appendix
    1: Equivalence relations; groups
    2: Convergence of distributions
    3: Dominated families of distributions
    4: The weak compactness theorem
    5: References
    Author Index
    Subject Index


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