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Full Description
This volume collects authoritative contributions on analytical methods and mathematical statistics. The methods presented include resampling techniques; the minimization of divergence; estimation theory and regression, eventually under shape or other constraints or long memory; and iterative approximations when the optimal solution is difficult to achieve. It also investigates probability distributions with respect to their stability, heavy-tailness, Fisher information and other aspects, both asymptotically and non-asymptotically. The book not only presents the latest mathematical and statistical methods and their extensions, but also offers solutions to real-world problems including option pricing. The selected, peer-reviewed contributions were originally presented at the workshop on Analytical Methods in Statistics, AMISTAT 2015, held in Prague, Czech Republic, November 10-13, 2015.
Contents
Preface.- A Weighted Bootstrap Procedure for Divergence Minimization Problems (Michel Broniatowski).- Asymptotic Analysis of Iterated 1-step Huber-skip M-estimators with Varying Cut-offs (Xiyu Jiao and Bent Nielsen).-Regression Quantile and Averaged Regression Quantile Processes (Jana Jurečková).- Stability and Heavy-tailness (Lev B. Klebanov).- Smooth Estimation of Error Distribution in Nonparametric Regression under Long Memory (Hira L. Koul and Lihong Wang).- Testing Shape Constrains in Lasso Regularized Joinpoint Regression (Matúš Maciak).- Shape Constrained Regression in Sobolev Spaces with Application to Option Pricing (Michal Pešta and Zdeněk Hlávka).- On Existence of Explicit Asymptotically Normal Estimators in Non-Linear Regression Problems (Alexander Sakhanenko).- On the Behavior of the Risk of a LASSO-Type Estimator (Silvelyn Zwanzig and M. Rauf Ahmad).
