Long-term phonemic representations become audiovisual by mid-childhood.

In earlier work with adults, we showed that long-term phonemic representations are audiovisual, meaning that they contain information on typical mouth shape during articulation. Many aspects of audiovisual processing have a prolonged developmental course, often not reaching maturity until late adolescence. In this study, we examined the status of phonemic representations in two groups of children - 8-9-year-olds and 11-12-year-olds.

Group Feature Screening via the F Statistic.

Feature screening is crucial in the analysis of ultrahigh dimensional data, where the number of variables (features) is in an exponential order of the number of observations. In various ultrahigh dimensional data, variables are naturally grouped, giving us a good rationale to develop a screening method using joint effect of multiple variables. In this article, we propose a group screening procedure via the F-test statistic.

Impaired Audiovisual Representation of Phonemes in Children with Developmental Language Disorder.

We examined whether children with developmental language disorder (DLD) differed from their peers with typical development (TD) in the degree to which they encode information about a talker's mouth shape into long-term phonemic representations. Children watched a talker's face and listened to rare changes from [i] to [u] or the reverse. In the neutral condition, the talker's face had a closed mouth throughout.

POWERFUL TEST BASED ON CONDITIONAL EFFECTS FOR GENOME-WIDE SCREENING.

This paper considers testing procedures for screening large genome-wide data, where we examine hundreds of thousands of genetic variants, e.g., single nucleotide polymorphisms (SNP), on a quantitative phenotype.

Bayesian monotonic errors-in-variables models with applications to pathogen susceptibility testing.

Drug dilution (MIC) and disk diffusion (DIA) are the 2 most common antimicrobial susceptibility assays used by hospitals and clinics to determine an unknown pathogen's susceptibility to various antibiotics. Since only one assay is commonly used, it is important that the 2 assays give similar results. Calibration of the DIA assay to the MIC assay is typically done using the error-rate bounded method, which selects DIA breakpoints that minimize the observed discrepancies between the 2 assays.

Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models.

Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates. The resulting data have provided new insights into neuro-behaviour.

Inferring network structure in non-normal and mixed discrete-continuous genomic data.

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate.

Calcium-41: a technology for monitoring changes in bone mineral.

The rare, long-lived radiotracer, Ca, measured by accelerator mass spectrometry in the urine or serum following incorporation into the bone provides an ultra-sensitive tool to assess changes in bone calcium balance in response to an intervention. Changes in bone balance can be followed for years with one small dose that is both radiologically and biologically non-invasive. Sequential interventions can be compared, with greater precision than they can with biochemical markers of bone turnover and with greater power than with bone densitometry.

Images from Bits: Non-Iterative Image Reconstruction for Quanta Image Sensors.

A quanta image sensor (QIS) is a class of single-photon imaging devices that measure light intensity using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these bits.

Determination of disk diffusion susceptibility testing interpretive criteria using model-based analysis: development and implementation.

The determination of diffusion test breakpoints has become a challenging issue due to the increasing resistance of microorganisms to antibiotics. Currently, the most commonly-used method for determining these breakpoints is the modified error-rate bounded method. Its use has remained widespread despite the introduction of several model-based methods that have been shown superior in terms of precision and accuracy.

Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems.

In truncated polynomial spline or B-spline models where the covariates are measured with error, a fully Bayesian approach to model fitting requires the covariates and model parameters to be sampled at every Markov chain Monte Carlo iteration. Sampling the unobserved covariates poses a major computational problem and usually Gibbs sampling is not possible. This forces the practitioner to use a Metropolis-Hastings step which might suffer from unacceptable performance due to poor mixing and might require careful tuning.

On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.

We introduce a nonparametric method for estimating non-gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a parallel structure to the gaussian graphical model that replaces the precision matrix by an additive precision operator.

A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet.

PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the "what and how" of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides.

Sparse Estimation of Conditional Graphical Models With Application to Gene Networks.

In many applications the graph structure in a network arises from two sources: intrinsic connections and connections due to external effects. We introduce a sparse estimation procedure for graphical models that is capable of isolating the intrinsic connections by removing the external effects. Technically, this is formulated as a graphical model, in which the external effects are modeled as predictors, and the graph is determined by the conditional precision matrix.