A course in large sample theory Thomas S. Ferguson
Publisher: Chapman & Hall

Ferguson, A Course in Large Sample Theory. A course in large sample theory (QA276.6.F47). Hence, the dependence condition (2.3) allows a stronger dependence within a larger class of random variables and demands more independence within a smaller class. A Course in Large Sample Theory (Chapman & Hall/CRC Texts in Statistical Science) . The central limit theorem is one of the most remarkable results of the theory of probability [1], which is critical to understand inferential statistics and hypothesis testing [2, 3]. Feuerverger, A., A consistent test for bivariate dependence. Mathematical statistics : a decision theoretic approach,. (Nagel's winning number was higher at 24, and Thaler's large experiment with .. A series of examples used in a course on large-sample theory illustrates this thesis. Ferguson, T.S., A Course in Large Sample Theory. $-small{ ext{Contains classical results on asymptotic properties of point estimators.}}$. A Course in Large Sample Theory is presented in four parts. Since our subjects are never chosen from random representative samples. Key Words: Large-sample theory, Statistical computing. It is my impression that most students approach a course in game theory with the . Ferguson, A Course in Large Sample Theory, Chapman & Hall, New York, NY, USA, 1996. 8 Large sample theory: A casual approach.