Mathematical Foundations for Finance and Insurance

Guest Instructor: 
C. Huang, X. Xiu
Time I: 
Monday,
10:00am to 12:00pm
Venue I: 
SPA1, R21a
Description: 

The course covers a part of mathematical statistics which deals with the limiting behavior of different sample statistics, U-statistics, M-, L- and R-Estimates. This course gives better understanding for the basic tools learned in the elementary Statistics I and II, like Law of Large Numbers, Central Limit Theorem, Kolmogorov-Smirnov and Cramer-von-Mises tests, sample mean and sample variance behavior, etc. This course is laying a bridge between the probability theory and the mathematical statistics by manipulating with “probability” theorems to obtain “statistical” theorems.

In the first part of the course we discuss basic tools of asymptotic theory in statistics: convergence in distribution, in probability, almost surely, in mean. We also consider main probability limit laws: LLN and CLT. Then we deal with the usual statistics computed from a sample: the sample distribution function, the sample moments, the sample quantiles, the order statistics. Properties, such as asymptotic normality and almost sure convergence will be derived in the lecture. Afterwards, comes the asymptotics of statistics concocted as transformations of vector of more basic statistics. Next part concerns statistics arising in classical parametric inference and contingency table analysis. These include maximum-likelihood estimates, likelihood-ratio tests, etc. Last part of the course treats U-statistics, statistics obtained as solutions of equations (M-estimates), linear function of order statistics (L-estimates) and rank statistics (R-estimates).

Literature:
R.J.Serfling, Approximation theorems of mathematical statistics, 1980, Wiley series in mathematics.

Exam: Oral exam

Credits: 
3.00
Program: 
Semester: 
Fall 2016
Affiliation: 
Humboldt-Universität zu Berlin
End date of the whole course: 
Monday, February 13, 2017 - 12:00pm