Principles and Theory for Data Mining and Machine Learning

Statistical Inference in Science

Growth Curve Models and Statistical Diagnostics

Sequential Analysis

Spatial Statistics and Modeling

Comparing Distributions

Semiparametric and Nonparametric Methods in Econometrics

Finite Mixture and Markov Switching Models

Maximum Penalized Likelihood Estimation

Exploring Multivariate Data with the Forward Search

Monte Carlo Strategies in Scientific Computing

Sampling Algorithms

Inequalities: Theory of Majorization and Its Applications

Inference in Hidden Markov Models

Recursive Partitioning and Applications

Dynamic Mixed Models for Familial Longitudinal Data

Statistical Demography and Forecasting

Models for Discrete Longitudinal Data

Modern Multidimensional Scaling

Statistical Analysis of Environmental Space-Time Processes

Nonparametric Functional Data Analysis

Principles and Theory for Data Mining and Machine Learning

Maximum Penalized Likelihood Estimation

Sample Survey Theory

Principal Component Analysis

Unified Methods for Censored Longitudinal Data and Causality

Reliability, Life Testing and the Prediction of Service Lives

Statistical Inference for Ergodic Diffusion Processes

Bayesian Reliability

Partial Identification of Probability Distributions

Selected Papers of Frederick Mosteller

Permutation, Parametric, and Bootstrap Tests of Hypotheses

Spectral Analysis of Large Dimensional Random Matrices

A Modern Theory of Factorial Design

Model-based Geostatistics

Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs