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 Bierens, Herman J., 1943
 New York : Cambridge University Press, 2004.
 Description
 Book — xvii, 323 pages ; 24 cm.
 Summary

 Part I. Probability and Measure:
 1. The Texas lotto
 2. Quality control
 3. Why do we need sigmaalgebras of events?
 4. Properties of algebras and sigmaalgebras
 5. Properties of probability measures
 6. The uniform probability measures
 7. Lebesque measure and Lebesque integral
 8. Random variables and their distributions
 9. Density functions
 10. Conditional probability, Bayes's rule, and independence
 11. Exercises: A. Common structure of the proofs of Theorems
 6 and 10, B. Extension of an outer measure to a probability measure Part II. Borel Measurability, Integration and Mathematical Expectations:
 12. Introduction
 13. Borel measurability
 14. Integral of Borel measurable functions with respect to a probability measure
 15. General measurability and integrals of random variables with respect to probability measures
 16. Mathematical expectation
 17. Some useful inequalities involving mathematical expectations
 18. Expectations of products of independent random variables
 19. Moment generating functions and characteristic functions
 20. Exercises: A. Uniqueness of characteristic functions Part III. Conditional Expectations:
 21. Introduction
 22. Properties of conditional expectations
 23. Conditional probability measures and conditional independence
 24. Conditioning on increasing sigmaalgebras
 25. Conditional expectations as the best forecast schemes
 26. Exercises A. Proof of theorem 22 Part IV. Distributions and Transformations:
 27. Discrete distributions
 28. Transformations of discrete random vectors
 29. Transformations of absolutely continuous random variables
 30. Transformations of absolutely continuous random vectors
 31. The normal distribution
 32. Distributions related to the normal distribution
 33. The uniform distribution and its relation to the standard normal distribution
 34. The gamma distribution
 35. Exercises: A. Tedious derivations B. Proof of theorem 29 Part V. The Multivariate Normal Distribution and its Application to Statistical Inference:
 36. Expectation and variance of random vectors
 37. The multivariate normal distribution
 38. Conditional distributions of multivariate normal random variables
 39. Independence of linear and quadratic transformations of multivariate normal random variables
 40. Distribution of quadratic forms of multivariate normal random variables
 41. Applications to statistical inference under normality
 42. Applications to regression analysis
 43. Exercises A. Proof of theorem 43 Part VI. Modes of Convergence:
 44. Introduction
 45. Convergence in probability and the weak law of large numbers
 46. Almost sure convergence, and the strong law of large numbers
 47. The uniform law of large numbers and its applications
 48. Convergence in distribution
 49. Convergence of characteristic functions
 50. The central limit theorem
 51. Stochastic boundedness, tightness, and the Op and opnotations
 52. Asymptotic normality of Mestimators
 53. Hypotheses testing
 54. Exercises: A. Proof of the uniform weak law of large numbers B. Almost sure convergence and strong laws of large numbers C. Convergence of characteristic functions and distributions Part VII. Dependent Laws of Large Numbers and Central Limit Theorems:
 55. Stationary and the world decomposition
 56. Weak laws of large numbers for stationary processes
 57. Mixing conditions
 58. Uniform weak laws of large numbers
 59. Dependent central limit theorems
 60. Exercises: A. Hilbert spaces Part VIII. Maximum Likelihood Theory
 61. Introduction
 62. Likelihood functions
 63. Examples
 64. Asymptotic properties if ML estimators
 65. Testing parameter restrictions
 66. Exercises.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780521834315 20170911
 Online
Business Library
Business Library  Status 

On reserve at Business Library  
HB139 .B527 2004  Unknown 2hour loan 
MGTECON60301
 Course
 MGTECON60301  Econometric Methods I
 Instructor(s)
 Da Cruz Correia Gardete, Pedro M
2. Introduction to the mathematical and statistical foundations of econometrics [electronic resource] [2004]
 Bierens, Herman J., 1943
 New York : Cambridge University Press, 2004.
 Description
 Book — xvii, 323 p.
Stanford Libraries
Stanford Libraries  Status 

On reserve at Business Library  
(no call number)  Unavailable 
MGTECON60301
 Course
 MGTECON60301  Econometric Methods I
 Instructor(s)
 Da Cruz Correia Gardete, Pedro M
3. Statistical inference [2002]
 Casella, George.
 2nd ed.  Australia ; Pacific Grove, CA : Thomson Learning, ©2002.
 Description
 Book — xxviii, 660 pages : ill. ; 25 cm.
 Summary

 Probability theory
 Transformations and expectations
 Common families of distributions
 Multiple random variables
 Properties of a random sample
 Principles of data reduction
 Point estimation
 Hypothesis testing
 Interval estimation
 Asymptotic evaluations
 Analysis of variance and regression
 Regression models.
(source: Nielsen Book Data) 9780534243128 20160617
 Online
Business Library
Business Library  Status 

On reserve at Business Library  
QA276 .C37 2002  Unknown 2hour loan 
MGTECON60301
 Course
 MGTECON60301  Econometric Methods I
 Instructor(s)
 Da Cruz Correia Gardete, Pedro M
4. Statistical inference [1975]
 Silvey, S. D. (Samuel David), author.
 Reprinted with corrections.  Boca Raton ; London : Chapman and Hall, 2003.
 Description
 Book — 191 pages : illustrations ; 24 cm.
 Summary

 Minimumvariance unbiased estimation. The method of least squares. The method of maximum likelihood. Confidence sets. Hypothesis testing. The likelihoodratio test and alternative 'largesample' equivalents of it. Sequential tests. Nonparametric methods. The Bayesian approach. An introduction to decision theory. Appendix A: some matrix results. Appendix B: the linear hypothesis.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780412138201 20180910
 Online
Business Library
Business Library  Status 

On reserve at Business Library  
QA276 .S497 1975  Unknown 2hour loan 
MGTECON60301
 Course
 MGTECON60301  Econometric Methods I
 Instructor(s)
 Da Cruz Correia Gardete, Pedro M
5. Statistical inference [1975]
 Silvey, S. D. (Samuel David)
 Reprinted with corrections.  London : Chapman and Hall ; New York : Wiley, 1975.
 Description
 Book — 1 online resource.
 Summary

 Minimumvariance unbiased estimation. The method of least squares. The method of maximum likelihood. Confidence sets. Hypothesis testing. The likelihoodratio test and alternative 'largesample' equivalents of it. Sequential tests. Nonparametric methods. The Bayesian approach. An introduction to decision theory. Appendix A: some matrix results. Appendix B: the linear hypothesis.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780412138201 20180910
 Online

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Business Library
Business Library  Status 

On reserve at Business Library  
(no call number)  Unknown 
MGTECON60301
 Course
 MGTECON60301  Econometric Methods I
 Instructor(s)
 Da Cruz Correia Gardete, Pedro M