when they do, Y. Liu and J. Hsu have the meth- functional linear models and apply them to data from
ods needed in “Testing for Efficacy in Primary the Sleep Heart Health Study. Functional predic-
and Secondary Endpoints by Partitioning tors that are trajectories having certain sample-path
Decision Paths.” properties in common with Brownian motion pro-
Transitioning to quantile regression, A. El vide the motivation for “Logistic Regression with
Ghouch and M. G. Genton use local-polynomial Brownian-Like Predictors” by M. A. Lindquist
estimation to obtain quantile regression estimators and I. W. McKeague. In “The Analysis of Two-
that transition naturally between parametric and Way Functional Data Using Two-Way Regularized
nonparametric estimators according to the data Singular Value Decompositions,” J. Z. Huang,
in “Local Polynomial Quantile Regression with H. Shen, and A. Buja extend one-way functional
Parametric Features.” Competing for the quantile- principal component analysis to two-way functional
regression crowd’s attention is “Competing Risks data via regularization of both the left and right sin-
Quantile Regression” by L. Peng and J. P. Fine. gular vectors in the singular-value decomposition of
C. M. Crainiceanu, A. Staicu, and C. Di the data matrix.
will keep you wide awake with “Generalized Dependent sequences of count data motivate
Multilevel Functional Regression,” in which K. Fokianos, A. Rahbek, and D. Tjøstheim’s
they introduce and study generalized multilevel research in “Poisson Autoregression.” Motivated by
applications to financial time series, N. Chan, S. X.
JASA book reviews for December Issue
Chen, C. L. Peng, and C. L. Yu author “Empirical
Likelihood Methods Based on Characteristic
Approximate Dynamic Programming: Solving the Curses of
Functions with Applications to Levy Processes.”
Dimensionality—Warren B. Powell
In “On Nonparametric Variance Estimation for
Asymptotic Analysis of Random Walks: Heavy-Tailed Distributions—A. A.
Second-Order Statistics of Inhomogeneous Spatial
Borovkov and K. A. Borovkov
Point Processes with a Known Parametric Intensity
Bayesian Biostatistics and Diagnostic Medicine—Lyle D. Broemeling
Form,” Y. Guan introduces variance estimation pro-
Counterfactuals and Causal Inference: Methods and Principles for Social
cedures for second-order statistics computed from a
Research—Stephen L. Morgan and Christopher Winship
single realization of an intensity reweighted station-
The Cult of Statistical Significance: How the Standard Error Costs Us Jobs,
ary spatial point process.
Justice, and Lives—Stephen T. Ziliak and Deirdre N. McCloskey
Rounding out the issue, H. Wang reveals
Demographic Forecasting—Federico Girosi and Gary King
a new use for an old algorithm in “Forward
Introductory Lectures on Fluctuations of Lévy Processes with Applications— Regression for Ultra-High Dimensional Variable
Andreas E. Kyprianou
Screening,” showing that forward regression can
Linear and Generalized Linear Mixed Models and Their Applications— identify all relevant predictors consistently, even
Jiming Jiang
if the predictor dimension is substantially larger
Matrix Methods in Data Mining and Pattern Recognition—Lars Eldén than the sample size. In “Empirical Likelihood
Model Selection and Model Averaging—Gerda Claeskens and Nils Lid
in Missing Data Problems,” J. Qin, B. Zhang,
Hjort and D. H. Y. Leung propose a unified empiri-
Multivariate Statistics: Exercises and Solutions—Wolfgang Härdle and
cal likelihood approach to missing data prob-
Zden ekˇ Hlávka lems. O. Boldea and J. R. Magnus study new
Partial Differential Equations for Probabilists—Daniel W. Stroock
variance estimators for mixture-model param-
The Probabilistic Method (3rd ed.)—Noga Alon and Joel H. Spencer
eters in “Maximum Likelihood Estimation of the
Multivariate Normal Mixture Model.” C. Zou and
The Statistical Analysis of Functional MRI Data—Nicole A. Lazar
P. Qiu show how to use a lasso to corral produc-
The Statistical Analysis of Recurrent Events—Richard J. Cook and Jerald
tion processes in “Multivariate Statistical Process
F. Lawless
Control Using LASSO.” M. Yuan and H. Zou
The Theory and Practice of Item Response Theory—R. J. de Ayala
develop efficient algorithms for computing non-
Applied Regression Analysis and Generalized Linear Models (2nd ed.)—
linear solution paths for general L1-regularization
John Fox
in “Efficient Global Approximation of Generalized
Introduction to Stochastic Calculus Applied to Finance (2nd ed.)—Damien
Nonlinear L1-Regularized Solution Paths and Its
Lamberton and Bernard Lapeyre
Applications.” Using both empirical and theoreti-
Matched Sampling for Causal Effects—Donald B. Rubin
cal arguments, P. Hall, D. M. Titterington, and
Permutation Methods: A Distance Function Approach (2nd ed.)—Paul W. J. Xue explore the properties of classifiers based
Mielke Jr. and Kenneth J. Berry on component-wise medians in “Median-Based
Sampling of Populations: Methods and Applications (4th ed.)—Paul S.
Classifiers for High-Dimensional Data.” L. Wang,
Levy and Stanley Lemeshow
B. Kai, and R. Li propose a novel, robust estima-
Time Series Analysis: With Applications in R (2nd ed.)—Jonathan D.
tion procedure for varying coefficient models based
Cryer and Kung-Sik Chan
on local ranks in “Local Rank Inference for Varying
Coefficient Models.” n
26 AmstAt News December 2009
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