Chapman & Hall/CRC Monographs on Statistics and Applied Probability – serie

Sparse Graphical Modeling for High Dimensional Data

A Paradigm of Conditional Independence Tests

Introduction to Time Series Modeling with Applications in R

Bayesian Nonparametrics for Causal Inference and Missing Data

Nonlinear Time Series

Semiparametric and Nonparametric Methods

Components of Variance

Subset Selection in Regression

Accelerated Life Models

Modeling and Statistical Analysis

Subjective Probability Models for Lifetimes

Classification

Semimartingales and their Statistical Inference

Time Series Models

In econometrics, finance and other fields

Multivariate Dependencies

Models, Analysis and Interpretation

Transformation and Weighting in Regression

Predictive Inference

Analysis of Quantal Response Data

Analysis of Repeated Measures

Analysis of Infectious Disease Data

Mixture Models

Parametric, Semiparametric, and New Directions

Replication and Evidence Factors in Observational Studies

Introduction to Time Series Modeling with Applications in R

Models for Dependent Time Series

Bayesian Inference for Partially Identified Models

Exploring the Limits of Limited Data

Multi-State Survival Models for Interval-Censored Data

Joint Modeling of Longitudinal and Time-to-Event Data

State-Space Methods for Time Series Analysis

Theory, Applications and Software

Nonparametric Models for Longitudinal Data

With Implementation in R

Multistate Models for the Analysis of Life History Data

Multivariate Kernel Smoothing and Its Applications

Missing and Modified Data in Nonparametric Estimation

With R Examples