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Integrating auxiliary data in optimal spatial design for species distribution modelling

METHODS IN ECOLOGY AND EVOLUTION 2018年第6期9卷 1626-1637页

作者： Reich, Brian J. Pacifici, Krishna Stallings, Jonathan W. North Carolina State Univ Dept Stat Raleigh NC 27695 USA

1. Traditional surveys used to create species distribution maps and estimate ecological relationships are expensive and time consuming. Citizen science offers a way to collect a massive amount of data at negligible cost and has been shown to be a useful supplement to traditional analyses. However, there remains a need to conduct formal surveys to firmly establish ecological relationships and trends. 2. In this paper, we investigate the use of auxiliary (e.g. citizen science) data as a guide to designing more efficient ecological surveys. Our aim is to explore the use of opportunistic data to inform spatial survey design through a novel objective function that minimizes misclassificaton rate (i.e. false positives and false negatives) of the estimated occupancy maps. We use an initial occupancy estimate from auxiliary data as the prior in a Bayesian spatial occupancy model, and an efficient posterior approximation that accounts for spatial dependence, covariate effects, and imperfect detection in an exchange algorithm to search for the optimal set of sampling locations to minimize misclassification rate. 3. We examine the optimal design as a function of the detection rate and quality of the citizen-science data, and compare this optimal design with several common ad hoc designs via an extensive simulation study. We then apply our method to eBird data for the brown-headed nuthatch in the Southeast US. 4. We argue that planning a survey with the use of auxiliary data improves estimation accuracy and may significantly reduce the costs of sampling.

关键词： Bayesian inference citizen science exchange algorithm geostatistics imperfect detection occupancy

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Efficient Bayesian inference for COM-Poisson regression models

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STATISTICS AND COMPUTING 2018年第3期28卷 595-608页

作者： Chanialidis, Charalampos Evers, Ludger Neocleous, Tereza Nobile, Agostino Univ Glasgow Sch Math & Stat Glasgow Lanark Scotland Univ York Dept Math York N Yorkshire England

COM-Poisson regression is an increasingly popular model for count data. Its main advantage is that it permits to model separately the mean and the variance of the counts, thus allowing the same covariate to affect in different ways the average level and the variability of the response variable. A key limiting factor to the use of the COM-Poisson distribution is the calculation of the normalisation constant: its accurate evaluation can be time-consuming and is not always feasible. We circumvent this problem, in the context of estimating a Bayesian COM-Poisson regression, by resorting to the exchange algorithm, an MCMC method applicable to situations where the sampling model (likelihood) can only be computed up to a normalisation constant. The algorithm requires to draw from the sampling model, which in the case of the COM-Poisson distribution can be done efficiently using rejection sampling. We illustrate the method and the benefits of using a Bayesian COM-Poisson regression model, through a simulation and two real-world data sets with different levels of dispersion.

关键词： Bayesian statistics Conway-Maxwell-Poisson regression Count data exchange algorithm Markov chain Monte Carlo Rejection sampling

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New Developments in Design of Experiments

New Developments in Design of Experiments

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作者： Peng, Jiayu PennState University Libraries

学位级别：Doctor of Philosophy

This dissertation consists of three projects on the design of *** first project is on order-of-addition experiments. In an order-of-addition problem, the output of a process depends on the order of adding different components into the system. The term ``order-of-addition'' originated from chemical experiments, in which the order of adding different reagents often affects the properties of final product. The importance of order-of-addition problem goes well beyond the area of chemistry though, for example, in the job scheduling field. An order-of-addition design is a set of pre-designed orders to test, so that the order effects can be most efficiently learned (in terms of some criteria), under certain experimental budget. In this project, we investigate the optimal design under different models of order effects, with the main results being: (1) a theory of D-optimal design under any order-of-addition linear model, and more specific theories under the pairwise-order model; (2) closed-form construction for a class of optimal and robust designs; (3) a fast and flexible algorithm for the construction of efficient designs with large number of *** second project is on the optimal design for estimating error variance. In screening experiments, the ``saturated design with center points'' is a conventional choice (in Response Surface Methodology). We propose an alternative design -- the projection of a larger Hadamard matrix. In this project, we show that with certain assumption, the proposed design achieves the most efficient estimate of not only the factor effects but also the error variance, and is thus preferable over the conventional choice. Under various common error distributions, theories and simulation results are given to demonstrate that the proposed design achieves a more reliable estimate of error variance, as well as a reliable significance testing. For more practical use cases, the optimal follow-up design problem is studied. Relevant opt

关键词： Optimal design Order-of-addition Run order Screening design exchange algorithm Traveling Salesman Problem

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Optimal experimental design for predator-prey functional response experiments

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2018年第144期15卷 20180186-20180186页

作者： Zhang, Jeff F. Papanikolaou, Nikos E. Kypraios, Theodore Drovandi, Christopher C. Queensland Univ Technol Sch Math Sci Brisbane Qld Australia Queensland Univ Technol Australian Ctr Excellence Math & Stat Frontiers A Brisbane Qld Australia Greek Minist Rural Dev & Food Directorate Plant Protect Athens Greece Agr Univ Athens Lab Agr Zool & Entomol Athens Greece Benaki Phytopathol Inst Athens Greece Univ Nottingham Sch Math Sci Nottingham England

Functional response models are important in understanding predator-prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller sample sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs;these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator Notonecta glauca (an aquatic insect) preying on Asellus aquaticus (a small crustacean), and another experiment involving a ladybird beetle (Propylea quatuordecimpunctata L.) preying on the black bean aphid (Aphis fabae Scopoli). As a by-product, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications.

关键词： D-optimality exchange algorithm Fisher information functional response optimal design robust design

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DP-Optimality in Terms of Multiple Criteria and Its Application to the Split-Plot Design

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JOURNAL OF QUALITY TECHNOLOGY 2017年第1期49卷 27-45页

作者： Cao, Yongtao Wulff, Shaun S. Robinson, Timothy J. Indiana Univ Penn Math Dept Stat Indiana PA 15705 USA Univ Wyoming Dept Stat Stat Laramie WY 82071 USA

Recent arguments have been made that optimal design criteria should incorporate pure error degrees of freedom for estimating unknown variance components. In this paper, we incorporate pure error, along with traditional design criteria, using Pareto optimization to identify a collection of optimal designs. This strategy demonstrates the trade-offs between these conflicting objectives without having to resort to weighted combinations of the criteria. The proposed approach also allows extension of the criteria for purposes of selecting optimal split-plot designs based on the D-criterion and pure error degrees of freedom.

关键词： D-Optimality exchange algorithm Lack-of-Fit Multi-Objective Optimization Optimal Design Pure Error Response Surface Model

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Design D-optimal event-related functional magnetic resonance imaging experiments

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2017年第1期66卷 73-91页

作者： Saleh, Moein Kao, Ming-Hung Pan, Rong Arizona State Univ Tempe AZ USA

New computer algorithms for finding D-optimal designs of stimulus sequence for functional magnetic resonance imaging (MRI) experiments are proposed. Although functional MRI data are commonly analysed by linear models, the construction of a functional MRI design matrix is much more complicated than in conventional experimental design problems. Inspired by the widely used exchange algorithm technique, our proposed approach implements a greedy search strategy over the vast functional MRI design space for a D-optimal design. Compared with a recently proposed genetic algorithm, our algorithms are superior in terms of computing time and achieved design efficiency in both single-objective and multiobjective problems. In addition, the algorithms proposed are sufficiently flexible to incorporate a constraint that requires the exact number of appearances of each type of stimulus in a design. This realistic design issue is unfortunately not well handled by existing methods.

关键词： exchange algorithm Functional magnetic resonance imaging experiments Genetic algorithm Greedy search Multiple objectives Optimal experimental design

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Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach

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REGIONAL SCIENCE AND URBAN ECONOMICS 2017年 63卷 97-120页

作者： Han, Xiaoyi Hsieh, Chih-Sheng Lee, Lung-fei Xiamen Univ MOE Key Lab Econometr Dept Publ Econ Wang Yanan Inst Studies Econ WISE Xiamen 361005 Peoples R China Xiamen Univ Fujian Key Lab Stat Sci Xiamen 361005 Peoples R China Chinese Univ Hong Kong Dept Econ 9-FEsther Lee Bldg Shatin Hong Kong Peoples R China Ohio State Univ Dept Econ 1945 N High St Columbus OH 43210 USA

In this paper we consider estimation and model selection of higher-order spatial autoregressive model by an efficient Bayesian approach. Based upon the exchange algorithm, we develop an efficient MCMC sampler, which does not rely on special features of spatial weights matrices and does not require the evaluation of the Jacobian determinant in the likelihood function. We also propose a computationally simple procedure to tackle nested model selection issues of higher-order spatial autoregressive models. We find that the exchange algorithm can be utilized to simplify the computation of Bayes factor through the Savage-Dickey density ratio. We apply the efficient estimation algorithm and the model selection procedure to study the "tournament competition" across Chinese cities and the spatial dependence of county-level voter participation rates in the 1980 U.S. presidential election.

关键词： Higher-order spatial autoregressive model exchange algorithm Bayesian estimation Bayes factor Savage-Dickey density ratio

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A NOVEL DESIGN OF BIORTHOGONAL GRAPH WAVELET FILTER BANKS

A NOVEL DESIGN OF BIORTHOGONAL GRAPH WAVELET FILTER BANKS

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IEEE International Conference on Image Processing

作者： Xi Zhang Department of Communication Engineering and Informatics The University of Electro-Communications

ISBN: (纸本)9781467399623

This paper proposes a novel method for designing compactly supported biorthogonal graph wavelet filter banks with flat spectral responses. We firstly construct a class of biorthogonal graph filter banks by using the polynomial half-band kernels, and then present a design method for the polynomial half-band kernel. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which ensures that the half-band kernel has the specified zeros at λ = 2, that is, the flatness constraints both in passband and stopband. Therefore, the resulting graph filters have the flat spectral responses. Furthermore, we apply the Remez exchange algorithm to minimize the spectral error by using the remaining degree of freedom. Finally, two design examples are given to demonstrate the effectiveness of the design method proposed in this paper.

关键词： Graph signal processing Graph wavelets Biorthogonal filter bank Polynomial half-band kernel design approach biorthogonal filter bank Degree of freedom exchange algorithm Banks Banks Filters Filter banks Passband Polynomial Seeds Line graph

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Model-Robust Designs for Quantile Regression

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2015年第509期110卷 233-245页

作者： Kong, Linglong Wiens, Douglas P. Univ Alberta Dept Math & Stat Sci Edmonton AB T6G 2G1 Canada

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function, and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. The methods are illustrated by example, and in a case study in which they are applied to growth charts.

关键词： Asymptotic mean squared error B-splines Compound design exchange algorithm Genetic algorithm Growth charts Heteroscedasticity Minimax bias Minimax mean squared error Nonlinear models Regression quantiles Uniformity

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D-optimal Factorial Designs under Generalized Linear Models

D-optimal Factorial Designs under Generalized Linear Models

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International Conference on Advances in Interdisciplinary Statistics and Combinatorics

作者： Yang, Jie Mandal, Abhyuday Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60680 USA Univ Georgia Dept Stat Athens GA 30602 USA

Generalized linear models (GLMs) have been used widely for modeling the mean response both for discrete and continuous random variables with an emphasis on categorical response. Recently Yang, Mandal and Majumdar (2013) considered full factorial and fractional factorial locally D-optimal designs for binary response and two-level experimental factors. In this article, we extend their results to a general setup with response belonging to a single-parameter exponential family and for multilevel predictors.

关键词： D-optimality exchange algorithm Factorial design Generalized linear model Lift-one algorithm Minimally supported design

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