Harald Cramer, Mathematical Methods of Statistics. Princeton
First Part: MATHEMATICAL INTRODUCTION
Chapter 1. General properties of sets
Chapter 2. Linear point sets
Chapter 3. Point sets in η dimensions
Chapter 4. The Lebesgue measure of a linear point set
Chapter 5. The Lebesgue integral for functions of one variable.
Chapter 6. Non-negative additive set functions in R
Chapter 7. The Lebesgue-Stieltjes integral for functions of one variable
Chapter 8. Lebeegue measure and other additive set functions in R
Chapter 9. The Lebesgue-Stieltjes integral for functions of n variables
Chapter 10. Fourier integrals
Chapter 11. Matrices, determinants and quadratic forms
Chapter 12. Miscellaneous complements
Second Part : RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Chapter 13. Statistics and probability
Chapter 14. Fundamental definitions and axioms
Chapter 15. General properties
Chapter 16. Various discrete distributions
Chapter 17. The normal distribution
Chapter 18. Various distributions related to the normal
Chapter 19. Further continuous distributions
Chapter 20. Some convergence theorems
Chapter 21. The two-dimensional case
Chapter 22. General properties of distributions in R¬n
Chapter 23. Regression and correlation in η variables
Chapter 24. The normal distribution
Third Part. STATISTICAL INFERENCE.
Chapter 25. Preliminary notions on sampling
Chapter 26. Statistical inference
Chapter 27. Characteristics of sampling distributions
Chapter 28. Asymptotic properties of Sampling distributions
Chapter 29. Exact sampling distributions
Chapter 30. Tests of goodness of fit and allied tests
Chapter 31. Tests of significance for parameters
Chapter 32. Classification of estimates
Chapter 33. Methods of estimation
Chapter 34. Confidence regions
Chapter 35. General theory of testing statistical hypotheses
Chapter 36. Analysis of variance
Chapter 37. Some regression problems