Information Theory and StatisticsCourier Corporation, 7 jul 1997 - 399 páginas Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition. |
Índice
CHAPTER PAGE | 1 |
PROPERTIES OF INFORMATION | 12 |
INEQUALITIES OF INFORMATION THEORY | 36 |
LIMITING PROPERTIES | 70 |
CHAPTER PAGE | 81 |
MULTINOMIAL POPULATIONS | 109 |
POISSON POPULATIONS | 142 |
CONTINGENCY TABLES | 155 |
CHAPTER PAGE | 239 |
MULTIVARIATE ANALYSIS THE MULTIVARIATE LINEAR HYPOTHESIS | 253 |
OTHER HYPOTHESES | 297 |
CHAPTER PAGE | 304 |
LINEAR DISCRIMINANT FUNCTIONS | 342 |
REFERENCES | 353 |
Loge n and n loge n for values of n from 1 through 1000 | 367 |
Fp1 p2 p₁ log+qı log | 378 |
CHAPTER PAGE | 159 |
MULTIVARIATE NORMAL POPULATIONS | 189 |
THE LINEAR HYPOTHESIS | 211 |
APPENDIX | 389 |
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Términos y frases comunes
absolutely continuous alternative hypothesis H₁ analysis in table analysis of variance asymptotically distributed binomial canonical correlation chapter 12 chapter 9 classification coefficients column Component due component in table computed conjugate distribution contingency table corollary covariance matrix dd(x defined degrees of freedom distributed as x² divergence dy(y E₁ equality equations example f₁(x Fisher H₁ H₂(R independent observations Information D.F. information theory k₁ k₂ lemma linear discriminant function log f(x log i=1 loge m₁ mathematical minimum discrimination information multinomial multinomial distribution N₁ n₂ noncentral normal distribution notation Note nữ null hypothesis H₂ p₁ partitioning Pijk Poisson distribution Poisson population Prob probability measure problem quadratic form regression sample Show space specified sufficient statistic Suppose theorem 2.1 unbiased estimates variables variates vectors Wilks x₁ x²-distribution Xijk y₁ μ₁ μ₂ Σ₁ σ² σι ΣΣ