Asymptotic Theory in Probability and Statistics with Applications (Volume 2 of the Advanced Lectures in Mathematics series)

A collection of 18 papers, many of which are surveys, on asymptotic theory in probability and statistics, with applications to a wide variety of problems. This volume comprises three parts: limit theorems, statistics and applications, and mathematical finance and insurance. It is intended for graduate students in probability and statistics, and for researchers in related areas.

Table of contents

Part I: Limit Theorems
Self-normalized Limit Theorems in Probability and Statistics
Asymptotic Analysis of Random Partitions
Limit Theorems on Adaptive Designs in Clinical Trials
Functional Limit Theorems for Gaussian Processes
Strong Local Nondeterminism and Sample Path Properties of Gaussian Random Fields
Large Deviations for Two-Parameter Gaussian Processes Related to Change-Point Analysis
Intersection Local Times: Large Deviations and Laws of the Iterated Logarithm
Limit Theorems for U-Statistics
Part II: Statistics and Applications
On the Inverse Problem for the t-Statistic
Statistical Analysis for Rounded Data
Piecewise Regression Models: Estimation Theory and Applications
Estimation in Partially Linear Models With Missing Data: A Review
Asymptotic Methods in Nonlinear Time Series Models
Nonparametric Classification and Probabilistic Classifier with Environmental and Remote Sensing Applications
Mixed Linear Model Approaches for Complex Trait Analysis
Part III: Mathematical Finance and Insurance
Inference and Computation for Stochastic Volatility Models Related to Option Pricing
A Selective Overview of Applications of Choquet Integrals
Some Recent Developments in Actuarial Science