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作者：Wierzbicki, Michael R.; Guo, Li-Bing; Du, Qing-Tao; Guo, Wensheng
作者单位：University of Pennsylvania; Guangdong Pharmaceutical University
摘要：Traditional Chinese herbal medications (TCHMs) are composed of a multitude of compounds and the identification of their active composition is an important area of research. Chromatography provides a visual representation of a TCHM sample's composition by outputting a curve characterized by spikes corresponding to compounds in the sample. Across different experimental conditions, the location of the spikes can be shifted, preventing direct comparison of curves and forcing compound identificatio...
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作者：Chen, Huaihou; Zeng, Donglin
作者单位：State University System of Florida; University of Florida; University of North Carolina; University of North Carolina Chapel Hill
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作者：Froelich, Markus; Huber, Martin
作者单位：University of Mannheim; IZA Institute Labor Economics; University of St Gallen
摘要：This article develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pretreatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment effect on the outcomes of compliers (the subpopulation whose treatment reacts on the instrument) in multiple periods based on inverse probability weighting. Treatment selection and attrition may depend on b...
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作者：Scarpa, Bruno; Dunson, David B.
作者单位：University of Padua; Duke University
摘要：In many applications involving functional data, prior information is available about the proportion of curves having different attributes. It is not straightforward to include such information in existing procedures for functional data analysis. Generalizing the functional Dirichlet process (FDP), we propose a class of stick-breaking priors for distributions of functions. These priors incorporate functional atoms drawn from constrained stochastic processes. The stick-breaking weights are speci...
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作者：Barthelme, Simon; Chopin, Nicolas
作者单位：University of Geneva; Institut Polytechnique de Paris; ENSAE Paris
摘要：Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistical inference. In the case where such models can be efficiently simulated, Bayesian inference is still possible thanks to the approximate Bayesian computation (ABC) algorithm. Although many refinements have been suggested, ABC inference is still far from routine. ABC is often excruciatingly slow due to very low accepta...
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作者：Fukumizu, Kenji; Leng, Chenlei
作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of Warwick; National University of Singapore
摘要：This article proposes a novel approach to linear dimension reduction for regression using nonparametric estimation with positive-definite kernels or reproducing kernel Hilbert spaces (RKHSs). The purpose of the dimension reduction is to find such directions in the explanatory variables that explain the response sufficiently: this is called sufficient dimension reduction. The proposed method is based on an estimator for the gradient of the regression function considered for the feature vectors ...
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作者：Geenens, Gery
作者单位：University of New South Wales Sydney
摘要：Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well-known boundary bias issues a conventional kernel density estimator would necessarily face in this situation. Transforming the variable of interest into a variable whose density has unconstrained support, estimating that density, and obtaining an estimate of the density of the original variable through back-transformation, seems a natural idea to easily get rid of the bou...
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作者：Liu, Lan; Hudgens, Michael G.
作者单位：Harvard University; Harvard University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Recently, there has been increasing interest in making causal inference when interference is possible. In the presence of interference, treatment may have several types of effects. In this article, we consider inference about such effects when the population consists of groups of individuals where interference is possible within groups but not between groups. A two-stage randomization design is assumed where in the first stage groups are randomized to different treatment allocation strategies ...
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作者：Panaretos, Victor M.; Pham, Tung; Yao, Zhigang
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We revisit the problem of extending the notion of principal component analysis (PCA) to multivariate datasets that satisfy nonlinear constraints, therefore lying on Riemannian manifolds. Our aim is to determine curves on the manifold that retain their canonical interpretability as principal components, while at the same time being flexible enough to capture nongeodesic forms of variation. We introduce the concept of a principal flow, a curve on the manifold passing through the mean of the data...
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作者：Airoldi, Edoardo M.; Costa, Thiago; Bassetti, Federico; Leisen, Fabrizio; Guindani, Michele
作者单位：Harvard University; Harvard University; University of Pavia; University of Kent; University of Texas System; UTMD Anderson Cancer Center
摘要：Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of nonexchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dir...
