Springer Series in Statistics – serie

Elements of Statistical Learning

Data Mining, Inference, and Prediction, Second Edition

Time Series: Theory and Methods

Statistical Models Based on Counting Processes

Multivariate Analysis with LISREL

Partial Identification of Probability Distributions

Resampling Methods for Dependent Data

Introduction to Rare Event Simulation

Selected Papers of Frederick Mosteller

Permutation, Parametric, and Bootstrap Tests of Hypotheses

Life Distributions

Structure of Nonparametric, Semiparametric, and Parametric Families

Statistical Demography and Forecasting

Statistical Demography and Forecasting

Models for Discrete Longitudinal Data

Modern Multidimensional Scaling

Theory and Applications

Nonlinear Time Series

Nonparametric and Parametric Methods

Statistical Analysis of Environmental Space-Time Processes

Introduction to Copulas

Non-negative Matrices and Markov Chains

Nonparametric Functional Data Analysis

Theory and Practice

Sampling Algorithms

Modern Theory of Factorial Design

Semiparametric Theory and Missing Data

Reliability, Life Testing and the Prediction of Service Lives

For Engineers and Scientists

Model-based Geostatistics

Finite Mixture and Markov Switching Models

Stochastic Orders

Functional Data Analysis

Statistical Tools for Nonlinear Regression

A Practical Guide With S-PLUS and R Examples

Inequalities: Theory of Majorization and Its Applications