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38
Regionalscale assembly rules and biodiversity of coral reefs. Science 292:1532–1534
, 2001
"... Tropical reef Þshes and corals exhibit highly predictable patterns of taxonomic composition across the Indian and PaciÞc Oceans. Despite steep longitudinal and latitudinal gradients in total species richness, the composition of these key taxa is constrained within a remarkably narrow range of values ..."
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Cited by 54 (2 self)
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Tropical reef Þshes and corals exhibit highly predictable patterns of taxonomic composition across the Indian and PaciÞc Oceans. Despite steep longitudinal and latitudinal gradients in total species richness, the composition of these key taxa is constrained within a remarkably narrow range of values. Regionalscale variation in reef biodiversity is best explained by largescale patterns in the availability of shallowwater habitat. Once habitat area is accounted for, there is surprisingly little residual effect of latitude or longitude. Lowdiversity regions are most vulnerable to human impacts such as global warming, underscoring the urgent need for integrated management at multinational scales. Globally, both terrestrial and aquatic ecosystems are experiencing declining biodiversity (1, 2). This decline highlights the need to understand processes regulating diversity and the consequences of species loss for ecosystem function (1–5). In marine systems, coral reefs are among
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network
 Proc. 1st IEEE Computer Society Bioinformatics Conference
, 2002
"... We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric ..."
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Cited by 48 (19 self)
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We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes. 1.
Bayesian Smoothing and Regression Splines for Measurement Error Problems
 Journal of the American Statistical Association
, 2001
"... In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely dicult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this paper we describe Bayes ..."
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Cited by 40 (8 self)
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In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely dicult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this paper we describe Bayesian approaches to modeling a exible regression function when the predictor variable is measured with error. The regression function is modeled with smoothing splines and regression P{splines. Two methods are described for exploration of the posterior. The rst is called iterative conditional modes (ICM) and is only partially Bayesian. ICM uses a componentwise maximization routine to nd the mode of the posterior. It also serves to create starting values for the second method, which is fully Bayesian and uses Markov chain Monte Carlo techniques to generate observations from the joint posterior distribution. Using the MCMC approach has the advantage that interval estimates that directly model and adjust for the measurement error are easily calculated. We provide simulations with several nonlinear regression functions and provide an illustrative example. Our simulations indicate that the frequentist mean squared error properties of the fully Bayesian method are better than those of ICM and also of previously proposed frequentist methods, at least in the examples we have studied. KEY WORDS: Bayesian methods; Eciency; Errors in variables; Functional method; Generalized linear models; Kernel regression; Measurement error; Nonparametric regression; P{splines; Regression Splines; SIMEX; Smoothing Splines; Structural modeling. Short title. Nonparametric Regression with Measurement Error Author Aliations Scott M. Berry (Email: scott@berryconsultants.com) is Statistical Scientist,...
F: Functional additive models
 J Am Stat Assoc
"... In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption ..."
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Cited by 29 (8 self)
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In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption and propose to replace it by an additive structure. This leads to a more widely applicable and much more flexible framework for functional regression models. The proposed functional additive regression models are suitable for both scalar and functional responses. The regularization needed for effective estimation of the regression parameter function is implemented through a projection on the eigenbasis of the covariance operator of the functional components in the model. The utilization of functional principal components in an additive rather than linear way leads to substantial broadening of the scope of functional regression models and emerges as a natural approach, as the uncorrelatedness of the functional principal components is shown to lead to a straightforward implementation of the functional additive model, just based on a sequence of onedimensional smoothing steps and without need for backfitting. This facilitates the theoretical analysis, and we establish asymptotic
Nonlinear and Nonparametric Regression and Instrumental
 Variables,Journal of the American Statistical Association
, 2004
"... We consider regression when the predictor is measured with error and an instrumental variable (IV) is available. The regression function can be modeled linearly, nonlinearly, or nonparametrically. Our major new result shows that the regression function and all parameters in the measurement error mod ..."
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Cited by 15 (6 self)
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We consider regression when the predictor is measured with error and an instrumental variable (IV) is available. The regression function can be modeled linearly, nonlinearly, or nonparametrically. Our major new result shows that the regression function and all parameters in the measurement error model are identified under relatively weak conditions, much weaker than previously known to imply identifiability. In addition, we exploit a characterization of the IV estimator as a classical “correction for attenuation ” method based on a particular estimate of the variance of the measurement error. This estimate of the measurement error variance allows us to construct functional nonparametric regression estimators making no assumptions about the distribution of the unobserved predictor and structural estimators that use parametric assumptions about this distribution. The functional estimators uses simulation extrapolation or deconvolution kernels and the structural method uses Bayesian Markov chain Monte Carlo. The Bayesian estimator is found to significantly outperform the functional approach.
2007): “Nonparametric matching and efficient estimators of homothetically separable functions
 Econometrica
"... For vectors x and w, letr(x, w) be a function that can be nonparametrically estimated consistently and asymptotically normally. We provide consistent, asymptotically normal estimators for the functions g and h, where r(x, w) =h[g(x),w], g is linearly homogeneous and h is monotonic in g. This framewo ..."
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Cited by 10 (4 self)
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For vectors x and w, letr(x, w) be a function that can be nonparametrically estimated consistently and asymptotically normally. We provide consistent, asymptotically normal estimators for the functions g and h, where r(x, w) =h[g(x),w], g is linearly homogeneous and h is monotonic in g. This framework encompasses homothetic and homothetically separable functions. Such models reduce the curse of dimensionality, provide a natural generalization of linear index models, and are widely used in utility, production, and cost function applications. One of our estimator’s of g is oracle efficient, achieving the same performance as an estimator based on local least squares knowing h. We provide simulation evidence on the small sample performance of our estimators, and an empirical production function application.
NONLINEAR METHODS OF CARDIOVASCULAR PHYSICS AND THEIR CLINICAL APPLICABILITY
, 2006
"... In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, ..."
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Cited by 9 (6 self)
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In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring, diagnosis, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.
Estimating Structural Exchange Rate Models By Artificial Neural Networks
, 1998
"... this paper. During the past few years there has been a noticeable increase of ANN applications in economics and finance (Trippi and Turban, 1993; Rehkugler and Zimmermann, 1994). However, no paper has been published on the application of ANNs to structural exchange rate modelling. Recently, some pap ..."
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Cited by 6 (1 self)
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this paper. During the past few years there has been a noticeable increase of ANN applications in economics and finance (Trippi and Turban, 1993; Rehkugler and Zimmermann, 1994). However, no paper has been published on the application of ANNs to structural exchange rate modelling. Recently, some papers have attempted to forecast currency exchange rates by ANNs, but they are not based on specific structural exchange rate (determination) models and are either based on nonlinear and nonparametric trading issues (Baestaens et al., 1994; Deboeck, 1994; Dunis, 1995) or are of a purely empirical nature (Refenes et al., 1993; Kuan andLiu,1995).Hencethereisaneedforaneconometric
www.mdpi.com/journal/ijerph Climate Change and Vectorborne Diseases: An Economic Impact Analysis of Malaria in Africa
, 2011
"... Abstract: A semiparametric econometric model is used to study the relationship between malaria cases and climatic factors in 25 African countries. Results show that a marginal change in temperature and precipitation levels would lead to a significant change in the number of malaria cases for most c ..."
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Cited by 3 (0 self)
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Abstract: A semiparametric econometric model is used to study the relationship between malaria cases and climatic factors in 25 African countries. Results show that a marginal change in temperature and precipitation levels would lead to a significant change in the number of malaria cases for most countries by the end of the century. Consistent with the existing biophysical malaria model results, the projected effects of climate change are mixed. Our model projects that some countries will see an increase in malaria cases but others will see a decrease. We estimate projected malaria inpatient and outpatient treatment costs as a proportion of annual 2000 health expenditures per 1,000 people. We found that even under minimal climate change scenario, some countries may see their inpatient treatment cost of malaria increase more than 20%. Keywords malaria and climate change; semiparametric modeling; cost of malaria treatment Int. J. Environ. Res. Public Health 2011, 8 914 1.