Transitioning to quantile regression, A. El Ghouch and M. G. Genton use local-polynomial estimation to obtain quantile regression estimators that transition naturally between parametric and nonparametric estimators according to the data in “Local Polynomial Quantile Regression with Parametric Features.” Competing for the quantile-regression crowd’s attention is “Competing Risks Quantile Regression” by L. Peng and J. P. Fine.

C. M. Crainiceanu, A. Staicu, and C. Di will keep you wide awake with “Generalized Multilevel Functional Regression,” in which they introduce and study generalized multilevel functional linear models and apply them to data from the Sleep Heart Health Study. Functional predictors that are trajectories having certain sample-path properties in common with Brownian motion provide the motivation for “Logistic Regression with Brownian-Like Predictors” by M. A. Lindquist and I. W. McKeague. In “The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions,” J. Z. Huang, H. Shen, and A. Buja extend one-way functional principal component analysis to two-way functional data via regularization of both the left and right singular vectors in the singular-value decomposition of the data matrix.

Dependent sequences of count data motivate K. Fokianos, A. Rahbek, and D. Tjøstheim’s research in “Poisson Autoregression.” Motivated by applications to financial time series, N. Chan, S. X. Chen, C. L. Peng, and C. L. Yu author “Empirical Likelihood Methods Based on Characteristic Functions with Applications to Levy Processes.” In “On Nonparametric Variance Estimation for Second-Order Statistics of Inhomogeneous Spatial Point Processes with a Known Parametric Intensity Form,” Y. Guan introduces variance estimation procedures for second-order statistics computed from a single realization of an intensity reweighted stationary spatial point process.

Rounding out the issue, H. Wang reveals a new use for an old algorithm in “Forward Regression for Ultra-High Dimensional Variable Screening,” showing that forward regression can identify all relevant predictors consistently, even if the predictor dimension is substantially larger than the sample size. In “Empirical Likelihood in Missing Data Problems,” J. Qin, B. Zhang, and D. H. Y. Leung propose a unified empirical likelihood approach to missing data problems. O. Boldea and J. R. Magnus study new variance estimators for mixture-model parameters in “Maximum Likelihood Estimation of the Multivariate Normal Mixture Model.” C. Zou and P. Qiu show how to use a lasso to corral production processes in “Multivariate Statistical Process Control Using LASSO.” M. Yuan and H. Zou develop efficient algorithms for computing nonlinear solution paths for general L1-regularization in “Efficient Global Approximation of Generalized Nonlinear L1-Regularized Solution Paths and Its Applications.” Using both empirical and theoretical arguments, P. Hall, D. M. Titterington, and J. Xue explore the properties of classifiers based on component-wise medians in “Median-Based Classifiers for High-Dimensional Data.” L. Wang, B. Kai, and R. Li propose a novel, robust estimation procedure for varying coefficient models based on local ranks in “Local Rank Inference for Varying Coefficient Models.”

JASA Book Reviews for December Issue
Approximate Dynamic Programming: Solving the Curses of Dimensionality—Warren B. Powell

Asymptotic Analysis of Random Walks: Heavy-Tailed Distributions—A. A. Borovkov and K. A. Borovkov

Bayesian Biostatistics and Diagnostic Medicine—Lyle D. Broemeling

Counterfactuals and Causal Inference: Methods and Principles for Social Research—Stephen L. Morgan and Christopher Winship

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives—Stephen T. Ziliak and Deirdre N. McCloskey

Demographic Forecasting—Federico Girosi and Gary King

Introductory Lectures on Fluctuations of Lévy Processes with Applications—Andreas E. Kyprianou

Linear and Generalized Linear Mixed Models and Their Applications—Jiming Jiang

Matrix Methods in Data Mining and Pattern Recognition—Lars Eldén

Model Selection and Model Averaging—Gerda Claeskens and Nils Lid Hjort

Multivariate Statistics: Exercises and Solutions—Wolfgang Härdle and Zdenek Hlávka

Partial Differential Equations for Probabilists—Daniel W. Stroock

The Probabilistic Method (3rd ed.)—Noga Alon and Joel H. Spencer

The Statistical Analysis of Functional MRI Data—Nicole A. Lazar

The Statistical Analysis of Recurrent Events—Richard J. Cook and Jerald F. Lawless

The Theory and Practice of Item Response Theory—R. J. de Ayala

Applied Regression Analysis and Generalized Linear Models (2nd ed.)—John Fox

Introduction to Stochastic Calculus Applied to Finance (2nd ed.)—Damien Lamberton and Bernard Lapeyre

Matched Sampling for Causal Effects—Donald B. Rubin

Permutation Methods: A Distance Function Approach (2nd ed.)—Paul W. Mielke Jr. and Kenneth J. Berry

Sampling of Populations: Methods and Applications (4th ed.)—Paul S. Levy and Stanley Lemeshow

Time Series Analysis: With Applications in R (2nd ed.)—Jonathan D. Cryer and Kung-Sik Chan