Raymond K. W. Wong

Effect of water content in a canned food on voluntary food intake and body weight in cats

OBJECTIVE To determine whether water content in a canned food diet induces decreases in voluntary energy intake (EI) or body weight (BW) in cats fed ad libitum. ANIMALS 16 sexually intact male domestic shorthair cats. PROCEDURES Maintenance EI was determined for 2 months in 10 weight-stable cats consuming a control diet (typical colony diet). Cats were allocated into 2 groups of equal BW and fed a canned diet (with-water [WW] diet) or a freeze-dried version of the canned diet (low-water [LW] diet) twice daily. Diets were identical in nutrient profile on a dry-matter basis. Each dietary treatment period of the crossover experiment lasted 3 weeks, with a 3-week washout period between diets. Body composition measurements were determined by use of deuterium oxide at the end of each dietary treatment. Daily food intake was measured for determination of dry-matter intake and EI. Six other cats were used in preference tests for the 3 diets. RESULTS EI was significantly decreased for the WW diet (mean ± SD, 1,053.0 ± 274.9 kJ/d), compared with EI for the LW diet (1,413.8 ± 345.8 kJ/d). Cats had a significant decrease in BW during consumption of the WW diet. Body composition was unaltered by diet. In short-term preference tests, cats ate significantly more of the WW than the LW diet. CONCLUSIONS AND CLINICAL RELEVANCE Bulk water in the WW diet stimulated decreases in EI and BW in cats. The impact of water content on energy density and food consumption may help promote weight loss in cats.

Robust Estimation for Generalized Additive Models

This article studies M-type estimators for fitting robust generalized additive models in the presence of anomalous data. A new theoretical construct is developed to connect the costly M-type estimation with least-squares type calculations. Its asymptotic properties are studied and used to motivate a computational algorithm. The main idea is to decompose the overall M-type estimation problem into a sequence of well-studied conventional additive model fittings. The resulting algorithm is fast and stable, can be paired with different nonparametric smoothers, and can also be applied to cases with multiple covariates. As another contribution of this article, automatic methods for smoothing parameter selection are proposed. These methods are designed to be resistant to outliers. The empirical performance of the proposed methodology is illustrated via both simulation experiments and real data analysis. Supplementary materials are available online.

A Full Bayesian Approach for Boolean Genetic Network Inference

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

Fiber direction estimation, smoothing and tracking in diffusion MRI

Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and noninvasive manner through measuring water diffusion. The contribution of this paper is threefold. First, it proposes a new method to identify and estimate multiple diffusion directions within a voxel through a new and identifiable parametrization of the widely used multi-tensor model. Unlike many existing methods, this method focuses on the estimation of diffusion directions rather than the diffusion tensors. Second, this paper proposes a novel direction smoothing method which greatly improves direction estimation in regions with crossing fibers. This smoothing method is shown to have excellent theoretical and empirical properties. Last, this paper develops a fiber tracking algorithm that can handle multiple directions within a voxel. The overall methodology is illustrated with simulated data and a data set collected for the study of Alzheimers disease by the Alzheimers Disease Neuroimaging Initiative (ADNI).

Nonparametric Cepstrum Estimation via Optimal Risk Smoothing

This paper proposes a new cepstrum estimation procedure that is capable of producing smoother and improved cepstrum estimates without the use of any parametric modeling. This procedure consists of two main steps: In the first step, it applies a so-called grid transformation to the empirical cepstral coefficients, while in the second step it nonparametrically smooths the transformed coefficients with local linear regression. The Steins unbiased risk estimation (SURE) approach is adopted to select both the extent of the grid transformation and the amount of smoothing. It is shown that the use of this SURE selection method for the current problem is asymptotically optimal in a well-defined sense. Lastly, the good practical performance of the new cepstrum estimation procedure is demonstrated via numerical experiments.

Partially Linear Functional Additive Models for Multivariate Functional Data

ABSTRACT We investigate a class of partially linear functional additive models (PLFAM) that predicts a scalar response by both parametric effects of a multivariate predictor and nonparametric effects of a multivariate functional predictor. We jointly model multiple functional predictors that are cross-correlated using multivariate functional principal component analysis (mFPCA), and model the nonparametric effects of the principal component scores as additive components in the PLFAM. To address the high-dimensional nature of functional data, we let the number of mFPCA components diverge to infinity with the sample size, and adopt the component selection and smoothing operator (COSSO) penalty to select relevant components and regularize the fitting. A fundamental difference between our framework and the existing high-dimensional additive models is that the mFPCA scores are estimated with error, and the magnitude of measurement error increases with the order of mFPCA. We establish the asymptotic convergence rate for our estimator, while allowing the number of components diverge. When the number of additive components is fixed, we also establish the asymptotic distribution for the partially linear coefficients. The practical performance of the proposed methods is illustrated via simulation studies and a crop yield prediction application. Supplementary materials for this article are available online.

Structural break estimation of noisy sinusoidal signals

In this article we consider the problem of partitioning a signal sequence into a set of signal sub-sequences, in such a way that each sub-sequence can be adequately modeled by a superposition of different sinusoids. In our formulation, the number of sub-sequences, the points at which two adjacent sub-sequences join, as well as the sinusoid composition in each sub-sequence are assumed unknown. We recast this problem as a statistical model selection problem, and invoke the minimum description length principle to construct estimators for these unknowns. As to be shown, these estimators are defined as the joint optimizer of a relatively complex objective function, and a genetic algorithm is developed for solving the corresponding optimization problem. The empirical performance of the resulting partitioning procedure is evaluated by a set of numerical experiments. The procedure is also applied to aid solving a classification problem that involves earthquake and explosion data.

An Advanced Statistical Approach to Data-Driven Earthquake Engineering

ABSTRACT Decades-long experimental databases become accessible in global earthquake engineering community. Yet, complex interactions of a multitude of variables pose formidable challenges to data-driven research. We embarked upon developing an advanced statistical learning and prediction framework with the generalized additive model (GAM). We showed promising performance of GAM with applications to existing RC shear wall databases. Without any prejudice, GAM can predict structural responses accurately using raw databases, and also can identify salient attributes. This study addresses computational implementation and parallel processing, and all codes are made publicly available to promote data-driven research of earthquake engineering community.

Nonparametric operator-regularized covariance function estimation for functional data

In functional data analysis (FDA), covariance function is fundamental not only as a critical quantity for understanding elementary aspects of functional data but also as an indispensable ingredient for many advanced FDA methods. This paper develops a new class of nonparametric covariance function estimators in terms of various spectral regularizations of an operator associated with a reproducing kernel Hilbert space. Despite their nonparametric nature, the covariance estimators are automatically positive semi-definite without any additional modification steps. An unconventional representer theorem is established to provide a finite dimensional representation for this class of covariance estimators, which leads to a closed-form expression of the corresponding

Kernel-based covariate functional balancing for observational studies

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