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338
Rapid worldwide depletion of predatory fish communities.
 Nature,
, 2003
"... Serious concerns have been raised about the ecological effects of industrialized fishing Ecological communities on continental shelves and in the open ocean contribute almost half of the planet's primary production 9 , and sustain threequarters of global fishery yields 1 . The widespread dec ..."
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Cited by 367 (7 self)
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Serious concerns have been raised about the ecological effects of industrialized fishing Ecological communities on continental shelves and in the open ocean contribute almost half of the planet's primary production 9 , and sustain threequarters of global fishery yields 1 . The widespread decline and collapse of major fish stocks has sparked concerns about the effects of overfishing on these communities. Historical data from coastal ecosystems suggest that losses of large predatory fishes, as well as mammals and reptiles, were especially pronounced, and precipitated marked changes in coastal ecosystem structure and function 5 . Such baseline information is scarce for shelf and oceanic ecosystems. Although there is an understanding of the magnitude of the decline in single stocks 10 , it is an open question how entire communities have responded to
The Analysis of Designed Experiments and Longitudinal Data Using Smoothing Splines
, 1997
"... this paper provides the mechanism for including cubic smoothing splines in models for the analysis of designed experiments and longitudinal data. Thus nonlinear curves can be included with random effects and random coefficients, and this leads to very flexible and informative modelling within the li ..."
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Cited by 58 (4 self)
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this paper provides the mechanism for including cubic smoothing splines in models for the analysis of designed experiments and longitudinal data. Thus nonlinear curves can be included with random effects and random coefficients, and this leads to very flexible and informative modelling within the linear mixed model framework. Variance heterogeneity can also be accommodated. The advantage of using the cubic smoothing spline in the case of longitudinal data is particularly pronounced, because covariance modelling is achieved implicitly as for random coefficient models. Several examples are considered to illustrate the ideas.
Bayesian MixedEffects Models for Recommender Systems
 In ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation
, 1999
"... We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the coldstart issue and can address both scalability issues as well as sparse ratings. However, our earl ..."
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Cited by 55 (5 self)
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We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the coldstart issue and can address both scalability issues as well as sparse ratings. However, our early experiments have shown mixed results. 1 Introduction Recommender systems have emerged as an important application area and have been the focus of considerable recent academic and commercial interest. The 1997 special issue of the Communications of the ACM [14] contains some key papers. Other important contributions include [2], [4], [8], [13], [16], [9], [1], [12], and [15]. In addition, many online retailers are using this technology to recommend new items to their customers, based on what they have bought in the past. Currently, most recommender systems are either contentbased or collaborative, depending on the type of information that the system uses to recommend items to a user. Co...
Functional Modeling and Classification of Longitudinal Data
"... We review and extend some statistical tools that have proved useful for analyzing functional data. Functional data analysis primarily is designed for the analysis of random trajectories and infinitedimensional data, and there exists a need for the development of adequate statistical estimation and ..."
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Cited by 41 (11 self)
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We review and extend some statistical tools that have proved useful for analyzing functional data. Functional data analysis primarily is designed for the analysis of random trajectories and infinitedimensional data, and there exists a need for the development of adequate statistical estimation and inference techniques. While this field is in flux, some methods have proven useful. These include warping methods, functional principal component analysis, and conditioning under Gaussian assumptions for the case of sparse data. The latter is a recent development that may provide a bridge between functional and more classical longitudinal data analysis. Besides presenting a brief review of functional principal components and functional regression, we develop some concepts for estimating functional principal component scores in the sparse situation. An extension of the socalled generalized functional linear model to the case of sparse longitudinal predictors is proposed. This extension includes functional binary regression models for longitudinal data and is illustrated with data on primary biliary cirrhosis.
Have multilevel models been structural equation models all along
 Multivariate Behavioral Research
, 2003
"... A core assumption of the standard multiple regression model is independence of residuals, the violation of which results in biased standard errors and test statistics. The structural equation model (SEM) generalizes the regression model in several key ways, but the SEM also assumes independence of ..."
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Cited by 38 (2 self)
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A core assumption of the standard multiple regression model is independence of residuals, the violation of which results in biased standard errors and test statistics. The structural equation model (SEM) generalizes the regression model in several key ways, but the SEM also assumes independence of residuals. The multilevel model (MLM) was developed to extend the regression model to dependent data structures. Attempts have been made to extend the SEM in similar ways, but several complications currently limit the general application of these techniques in practice. Interestingly, it is well known that under a broad set of conditions SEM and MLM longitudinal "growth curve" models are analytically and empirically identical. This is intriguing given the clear violation of independence in growth modeling that does not detrimentally affect the standard SEM. Better understanding the source and potential implications of this isomorphism is my focus here. I begin by exploring why SEM and MLM are analytically equivalent methods in the presence of nesting due to repeated observations over time. I then capitalize on this equivalency to allow for the extension of SEMs to a general class of nested data structures. I conclude with a description of potential opportunities for multilevel SEMs and directions for future developments. The structural equation model (SEM) is a flexible and powerful analytical method that has become a mainstay in many areas of social science research. The generality of this approach is evidenced in the ability to parameterize the SEM to estimate well known members of the general linear modeling (GLM) family including the ttest, ANOVA, ANCOVA, MANOVA, MANCOVA, and the multiple regression model. However, the
Polynomial spline estimation and inference for varying coefficient models with longitudinal data
 Statist. Sinica
, 2004
"... We consider nonparametric estimation of coefficient functions in a varying coefficient model of the form Yij = XTi (tij)β(tij)+ i(tij) based on longitudinal observations {(Yij, Xi(tij), tij), i = 1,..., n, j = 1,..., ni}, where tij and ni are the time of the jth measurement and the number of repeate ..."
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Cited by 34 (4 self)
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We consider nonparametric estimation of coefficient functions in a varying coefficient model of the form Yij = XTi (tij)β(tij)+ i(tij) based on longitudinal observations {(Yij, Xi(tij), tij), i = 1,..., n, j = 1,..., ni}, where tij and ni are the time of the jth measurement and the number of repeated measurements for the ith subject, and Yij and Xi(tij) = (Xi0(tij),..., XiL(tij))T for L ≥ 0 are the ith subject’s observed outcome and covariates at tij. We approximate each coefficient function by a polynomial spline and employ the least squares method to do the estimation. An asymptotic theory for the resulting estimates is established, including consistency, rate of convergence and asymptotic distribution. The asymptotic distribution results are used as a guideline to construct approximate confidence intervals and confidence bands for components of β(t). We also propose a polynomial spline estimate of the covariance structure of (t), which is used to estimate the variance of the spline estimate β̂(t). A data example in epidemiology and a simulation study are used to demonstrate our methods. Key words and phrases: Asymptotic normality; confidence intervals; nonparametric regression; repeated measurements, varying coefficient models. 1 1
Linear mixed models with flexible distributions of random effects for longitudinal data.
 Biometrics
, 2001
"... SUMMARY. Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of amongindividual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gall ..."
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Cited by 31 (4 self)
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SUMMARY. Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of amongindividual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics 55, 363390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a userchosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study.
Population HIV1 dynamics in vivo: applicable models and inferential tools for virological data from AIDS clinical trials
 Biometrics
, 1999
"... In this paper we introduce a novel application of hierarchical nonlinear mixedeffect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV1 dynamics (HIV1 RNA copies) after initiation of potent antiviral treatments ..."
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Cited by 31 (8 self)
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In this paper we introduce a novel application of hierarchical nonlinear mixedeffect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV1 dynamics (HIV1 RNA copies) after initiation of potent antiviral treatments, and can also be justified by a biological compartment model for the interaction between HIV and its host cells. This kind of model enjoys both biological interpretability and mathematical simplicity after reparameterization and simplification. A model simplification procedure is proposed and illustrated through examples. We interpret and justify various simplified models based on clinical data taken during different phases of viral dynamics during antiviral treatments. We suggest the hierarchical nonlinear mixedeffect model approach for parameter estimation and other statistical inferences. In the context of an AIDS clinical trial involving patients treated with a combination of potent antiviral agents, we show how the models may be used to draw biologically relevant interpretations from repeated HIV1 RNA measurements and demonstrate the potential use of the models in clinical decisionmaking. ∗ Corresponding author’s
HIV dynamics: modeling, data analysis, and optimal treatment protocols
 J. Comput. Appl. Math
, 2005
"... We present an overview of some concepts and methodologies we believe useful in modeling HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical and statistical ..."
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Cited by 26 (10 self)
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We present an overview of some concepts and methodologies we believe useful in modeling HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical and statistical ideas relevant to Structured Treatment Interruptions (STI). Among these are model development and validation procedures including parameter estimation, data reduction and representation, and optimal control relative to STI. Results from initial attempts in each of these areas by an interdisciplinary team of applied mathematicians, statisticians and clinicians are presented. Key words: HIV models, parameter estimation, data and model reduction, structured treatment interruptions, optimal control
An inverse problem statistical methodology summary
 North Carolina State University
"... We discuss statistical and computational aspects of inverse or parameter estimation problems based on Ordinary Least Squares and Generalized Least Squares with appropriate corresponding data noise assumptions of constant variance and nonconstant variance (relative error), respectively. Among the t ..."
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Cited by 24 (18 self)
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We discuss statistical and computational aspects of inverse or parameter estimation problems based on Ordinary Least Squares and Generalized Least Squares with appropriate corresponding data noise assumptions of constant variance and nonconstant variance (relative error), respectively. Among the topics included here are mathematical model, statistical model and data assumptions, and some techniques (residual plots, sensitivity analysis, model comparison tests) for verifying these. The ideas are illustrated throughout with the popular logistic growth model of Verhulst and Pearl as well as with a recently developed population level model of pneumococcal disease spread.