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  [SJR: 0.985]   [H-I: 5]   [1 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Online) 2049-1573
   Published by John Wiley and Sons Homepage  [1589 journals]
  • Issue Information
    • PubDate: 2017-04-20T06:12:28.66425-05:0
      DOI: 10.1002/wene.229
  • Adaptively tuned particle swarm optimization with application to spatial
    • Authors: Matthew Simpson; Christopher K. Wikle, Scott H. Holan
      Abstract: Particle swarm optimization (PSO) algorithms are a class of heuristic optimization algorithms that are attractive for complex optimization problems. We propose using PSO to solve spatial design problems, e.g. choosing new locations to add to an existing monitoring network. Additionally, we introduce two new classes of PSO algorithms that perform well in a wide variety of circumstances, called adaptively tuned PSO and adaptively tuned bare bones PSO. To illustrate these algorithms, we apply them to a common spatial design problem: choosing new locations to add to an existing monitoring network. Specifically, we consider a network in the Houston, TX, area for monitoring ambient ozone levels, which have been linked to out-of-hospital cardiac arrest rates. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA
      PubDate: 2017-04-17T19:25:42.733323-05:
      DOI: 10.1002/sta4.142
  • On the selection consistency of Bayesian structured variable selection
    • Authors: Kaixu Yang; Xiaoxi Shen
      Abstract: A Bayesian variable selection framework is considered for analyzing image data. For the spatial dependencies to be modelled among the covariates, an Ising prior is assigned to the binary latent vector γ, which indicates whether a covariate should be selected or not. The selection process, that is, the estimation of γ, can be carried out with Gibbs sampler. Although the model has been used in many scientific applications, no theoretical development has been made. In this article, we established theories on the model selection consistency under mild conditions, which is an important theoretical property for high-dimensional variable selection. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-04-17T18:30:36.35643-05:0
      DOI: 10.1002/sta4.141
  • Robust quantile regression using a generalized class of skewed
    • Authors: Christian Galarza Morales; Victor Lachos Davila, Celso Barbosa Cabral, Luis Castro Cepero
      Abstract: It is well known that the widely popular mean regression model could be inadequate if the probability distribution of the observed responses do not follow a symmetric distribution. To deal with this situation, the quantile regression turns to be a more robust alternative for accommodating outliers and the misspecification of the error distribution because it characterizes the entire conditional distribution of the outcome variable. This paper presents a likelihood-based approach for the estimation of the regression quantiles based on a new family of skewed distributions. This family includes the skewed version of normal, Student-t, Laplace, contaminated normal and slash distribution, all with the zero quantile property for the error term and with a convenient and novel stochastic representation that facilitates the implementation of the expectation–maximization algorithm for maximum likelihood estimation of the pth quantile regression parameters. We evaluate the performance of the proposed expectation–maximization algorithm and the asymptotic properties of the maximum likelihood estimates through empirical experiments and application to a real-life dataset. The algorithm is implemented in the R package lqr, providing full estimation and inference for the parameters as well as simulation envelope plots useful for assessing the goodness of fit. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-03-15T00:30:31.492398-05:
      DOI: 10.1002/sta4.140
  • Covariate selection for multilevel models with missing data
    • Authors: Miguel Marino; Orfeu M. Buxton, Yi Li
      Pages: 31 - 46
      Abstract: Missing covariate data hamper variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods that are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data are present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyse the Healthy Directions–Small Business cancer prevention study, which evaluated a behavioural intervention programme targeting multiple risk-related behaviours in a working-class, multi-ethnic population. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-01-08T18:50:26.241275-05:
      DOI: 10.1002/sta4.133
  • A parametric model bridging between bounded and unbounded variograms
    • Authors: Martin Schlather; Olga Moreva
      Pages: 47 - 52
      Abstract: A simple variogram model with two parameters is presented that includes the power variogram for fractional Brownian motion, a modified De Wijsian model, the generalized Cauchy model and the multiquadric model. One parameter controls the sample path roughness of the process. The other parameter allows for a smooth transition between bounded and unbounded variograms, that is, between stationary and intrinsically stationary processes in a Gaussian framework, or between mixing and non-ergodic Brown–Resnick processes when modeling spatial extremes. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-07T18:00:37.543933-05:
      DOI: 10.1002/sta4.134
  • A Bayesian supervised dual-dimensionality reduction model for simultaneous
           decoding of LFP and spike train signals
    • Authors: Andrew Holbrook; Alexander Vandenberg-Rodes, Norbert Fortin, Babak Shahbaba
      Pages: 53 - 67
      Abstract: Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities—such as electroencephalogram, functional magnetic resonance imaging, local field potential (LFP) and spike trains—offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modelling of LFP and spike train data and present a novel Bayesian method for neural decoding to infer behavioural and experimental conditions. This model performs supervised dual-dimensionality reduction: it learns low-dimensional representations of two different sources of information that not only explain variation in the input data itself but also predict extraneuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential principal components analysis (PCA) and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone and combined LFP and spike train data. We compare two methods for modelling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-07T21:48:34.295858-05:
      DOI: 10.1002/sta4.137
  • “Stationary” point processes are uncommon on linear networks
    • Authors: Adrian Baddeley; Gopalan Nair, Suman Rakshit, Greg McSwiggan
      Pages: 68 - 78
      Abstract: Statistical methodology for analysing patterns of points on a network of lines, such as road traffic accident locations, often assumes that the underlying point process is “stationary” or “correlation-stationary.” However, such processes appear to be rare. In this paper, popular procedures for constructing a point process are adapted to linear networks: many of the resulting models are no longer stationary when distance is measured by the shortest path in the network. This undermines the rationale for popular statistical methods such as the K-function and pair correlation function. Alternative strategies are proposed, such as replacing the shortest-path distance by another metric on the network. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-08T18:20:31.12866-05:0
      DOI: 10.1002/sta4.135
  • A second look at inference for bivariate Skellam distributions
    • Authors: Sidi Allal Aissaoui; Christian Genest, Mhamed Mesfioui
      Pages: 79 - 87
      Abstract: Two bivariate extensions of the Skellam distribution were introduced in 2014 by a subset of the present authors, who also proposed moment estimators for the dependence parameters in these models. The limiting distribution of these estimators is established here, and their asymptotic efficiency is compared with that of the corresponding maximum likelihood estimators. © 2017 The
      Authors . Stat Published by John Wiley & Sons Ltd.
      PubDate: 2017-02-16T17:45:25.574195-05:
      DOI: 10.1002/sta4.136
  • A procedure to detect general association based on concentration of ranks
    • Authors: Pratyaydipta Rudra; Yihui Zhou, Fred A. Wright
      Pages: 88 - 101
      Abstract: In modern high-throughput applications, it is important to identify pairwise associations between variables and desirable to use methods that are powerful and sensitive to a variety of association relationships. We describe RankCover, a new non-parametric association test of association between two variables that measures the concentration of paired ranked points. Here, “concentration” is quantified using a disk-covering statistic similar to those employed in spatial data analysis. Considerations from the theory of Boolean coverage processes provide motivation, as well as an R2-like quantity to summarize strength of association. Analysis of simulated and real datasets demonstrates that the method is robust and often powerful in comparison with competing general association tests. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-16T17:50:29.948074-05:
      DOI: 10.1002/sta4.138
  • Accurate logistic variational message passing: algebraic and numerical
    • Authors: Tui H. Nolan; Matt P. Wand
      Pages: 102 - 112
      Abstract: We provide full algebraic and numerical details required for fitting accurate logistic likelihood regression-type models via variational message passing with factor graph fragments. Existing methodology of this type involves the Jaakkola–Jordan device, which is prone to poor accuracy. We examine two alternatives: the Saul–Jordan tilted bound device and conjugacy enforcement via multivariate normal prespecification of a key message. Both of these approaches appear in related literature. Our contributions facilitate immediate implementation within variational message passing schemes. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-03-09T03:50:30.04134-05:0
      DOI: 10.1002/sta4.139
  • An assessment of price convergence between natural gas and solar
           photovoltaic in the U.S. electricity market
    • Authors: Joseph Nyangon; John Byrne, Job Taminiau
      Abstract: The U.S. shale boom has exerted downward pressure on natural gas prices nationally, widened oil-to-gas price spreads, and accelerated coal-to-gas fuel substitution. One concern is the impact of the rising production of shale gas on further development of a domestic solar photovoltaic (PV) market. Specifically, will lower natural gas prices slow or even reverse the current rapid growth in the solar market? Using the Phillips–Sul convergence test, this paper investigates whether the levelized cost of energy (LCOE) of solar PV and natural gas electricity generation in the United States have converged. Using weekly Henry Hub-linked natural gas spot prices and utility PV system prices from 2010 to 2015, empirical tests for convergence are applied to examine the extent of spot market integration and the speed with which market forces move the two energy prices toward equilibrium. The paper also assesses the link between the MAC Solar Energy Index (SUNIDX) and the S&P GSCI natural gas index spot prices for evidence of market integration during 2007–2015. We conclude that PV and natural gas prices are not converging, and the two markets are not integrated nationally, but some level of integration could exist at regional and state levels that will need to be tested in future research. We also conclude that complementary use of the technologies is likely; while price convergence is not likely to occur soon, distinctive complementary benefits of each resource compared to each other (e.g., fast-start capabilities for gas and low price volatility for PV) will offer opportunities that expand market demand for both. WIREs Energy Environ 2017, 6:e238. doi: 10.1002/wene.238For further resources related to this article, please visit the WIREs website.Shale gas boom in Pennsylvania is a major driver of strong growth in natural gas production in counties such as Washington, Bradford, and Susquehanna with positive effects on (a) gas spot prices, (b) wages and local business activity, and (c) economic diversification and infrastructure development. A multidimensional market perspective is applied to study natural gas and solar PV markets in the United States for evidence of price convergence and integration using Phillips-Sul convergence test and Kalman filter time-varying analysis as well as assessing the performance of MAC Global Solar Energy Index (SUNIDX) and S&P GSCI (natural gas) indices. Modeled LCOE for solar PV and natural gas systems average 12.95¢/kWh and 9.5¢/kWh, respectively.
      PubDate: 2016-12-15T21:40:38.675798-05:
      DOI: 10.1002/wene.238
  • The bioliq process for producing synthetic transportation fuels
    • Authors: Nicolaus Dahmen; Johannes Abeln, Mark Eberhard, Thomas Kolb, Hans Leibold, Jörg Sauer, Dieter Stapf, Bernd Zimmerlin
      Abstract: Biofuels of the second generation can contribute significantly to the replacement of the currently used fossil energy carriers for transportation fuel production. The lignocellulosic biomass residues used do not compete with food and feed production, but have to be collected from wide-spread areas for industrial large-scale use. The two-stage gasification concept bioliq offers a solution to this problem. It aims at the conversion of low-grade residual biomass from agriculture and forestry into synthetic fuels and chemicals. Central element of the bioliq process development is the 2–5 MW pilot plant along the complete process chain: fast pyrolysis for pretreatment of biomass to obtain an energy dense, liquid intermediate fuel, high-pressure entrained flow gasification providing low methane synthesis gas free of tar, hot synthesis gas cleaning to separate acid gases, and contaminants as well as methanol/dimethyl ether and subsequent following gasoline synthesis. After construction and commissioning of the individual process steps with partners from industry, first production of synthetic fuel was successfully achieved in 2014. In addition to pilot plant operation for technology demonstration, a research and development network has been established providing the scientific basis for optimization and further development of the bioliq process as well as to explore new applications of the technologies and products involved. WIREs Energy Environ 2017, 6:e236. doi: 10.1002/wene.236For further resources related to this article, please visit the WIREs website.The decentralized/central bioliq concept.
      PubDate: 2016-11-03T02:36:33.946307-05:
      DOI: 10.1002/wene.236
  • Opportunities to encourage mobilization of sustainable bioenergy supply
    • Authors: C. Tattersall Smith; Brenna Lattimore, Göran Berndes, Niclas Scott Bentsen, Ioannis Dimitriou, J.W.A. (Hans) Langeveld, Evelyne Thiffault
      Abstract: Significant opportunities exist to reduce greenhouse gas emissions, increase domestic energy security, boost rural economies, and improve local environmental conditions through the deployment of sustainable bioenergy and bio-based product supply chains. There is currently a wide selection of possible feedstocks, a variety of conversion routes, and a number of different end products that can be produced at a range of scales. However, economic slowdown, low oil prices, lack of global political will, and lingering questions regarding land use change and the sustainability of bioenergy production systems provide a challenging global context to speed the pace of investment. The opinions expressed in this paper are derived from our collaboration within IEA Bioenergy to determine opportunities as well as barriers that need to be overcome to realize opportunities on a wider scale. This comprehensive and novel collaborative effort confirmed that feedstocks produced using logistically efficient production systems can be mobilized to make significant contributions to achieving global targets for bioenergy. At the same time, significant barriers to large-scale implementation exist in many regions. The mobilization potential identified in the study will depend on both increases in supply chain efficiencies and profits and strong policy support to increase stakeholder and investor confidence. WIREs Energy Environ 2017, 6:e237. doi: 10.1002/wene.237For further resources related to this article, please visit the WIREs website.Mobilizing sustainable bioenergy supply chains from agricultural residues, boreal and temperate forests, cultivated grasslands, lignocellulosic crops and biogas can be realized if advantage is taken of technical, institutional, economic and social opportunities to remove barriers and address challenges currently affecting the magnitude of bioenergy system deployment.
      PubDate: 2016-10-25T20:50:26.676736-05:
      DOI: 10.1002/wene.237
  • Catalytic fast pyrolysis of lignocellulosic biomass for aromatic
           production: chemistry, catalyst and process
    • Authors: Anqing Zheng; Liqun Jiang, Zengli Zhao, Zhen Huang, Kun Zhao, Guoqiang Wei, Haibin Li
      Abstract: Catalytic fast pyrolysis (CFP) is one of most promising technologies for aromatic production in a single-step process. In recent years, considerable efforts have been made on the CFP of biomass for aromatic productions. However, the successful commercialization of CFP has hindered by several technical barriers, such as the rational design of continuous reactor and the development of high efficient and stable zeolite catalysts. Here, we attempt to summarize the advances in CFP of biomass from four aspects: (1) reaction chemistry, (2) reactor and operating conditions for CFP, (3) catalysts for CFP, and (4) new processes for CFP. It is expected that this review could provide some guidance for solving the technical barriers aforementioned. WIREs Energy Environ 2017, 6:e234. doi: 10.1002/wene.234For further resources related to this article, please visit the WIREs website.
      PubDate: 2016-09-14T21:25:32.517046-05:
      DOI: 10.1002/wene.234
  • A review of synchrophasor applications in smart electric grid
    • Authors: H. Lee; Tushar, B. Cui, A. Mallikeswaran, P. Banerjee, A. K. Srivastava
      Abstract: The adoption of synchrophasor technology with ongoing smart grid activities has resulted in a transformation of the power system monitoring and control practices. Phasor measurement units (PMUs) synchronized by the global positioning system (GPS) provide time-stamped voltage and current phasors at faster rate for enhanced situational awareness and decision support compared to existing legacy technology and help in improving the reliability of the power system. The measurements from PMU can be used for number of possible applications and can be classified based on the time criticality requirements (e.g., adaptive protection vs state estimation (SE)), control center versus engineering analysis (e.g., state estimation vs model validation), monitoring versus control (e.g., angle monitoring vs stability control), and existing versus evolving (e.g., state estimation vs transient stability) applications. Some of these synchrophasor applications like monitoring and postmortem studies have been already adopted by power industry, some of the synchrophasor-based control applications are still being investigated. In this study, various power system applications supported by synchrophasor technology are reviewed and discussed. WIREs Energy Environ 2017, 6:e223. doi: 10.1002/wene.223For further resources related to this article, please visit the WIREs website.
      PubDate: 2016-07-21T01:00:42.220087-05:
      DOI: 10.1002/wene.223
  • Global electricity demand, generation, grid system, and renewable energy
           polices: a review
    • Authors: M. Hasanuzzaman; Ummu Salamah Zubir, Nur Iqtiyani Ilham, Hang Seng Che
      Abstract: The rising concerns on the impacts of greenhouse gas (GHG) emissions and global warming have forced the world to search for alternative clean and green energy resources. Renewable energy is one of the most sustainable forms of energy, and is an increasingly popular replacement to fossil fuels. In line with this, the world has witnessed tremendous growth in renewable energy technologies as well as unprecedented adoption of renewable-based electricity generation in the past few decades. This review paper focuses on the global energy demand, power generation, electricity production, electrical grid system as well as current global polices to move forward for renewable energy-based power generation. This work compiles the latest literature (i.e., journal articles, conference proceedings, reports, etc.) on global energy demand, power generation, electrical grid system as well as current global polices related to renewable energy. From the review, it is found that renewable energy is one of the potential resources to fulfill the future energy demand with mitigating GHG emissions and global warming. It is also found that many polices has been implementing globally to promote renewable energy-based power generation. WIREs Energy Environ 2017, 6:e222. doi: 10.1002/wene.222For further resources related to this article, please visit the WIREs website.
      PubDate: 2016-07-13T01:05:39.092837-05:
      DOI: 10.1002/wene.222
  • Issue Information
    • Pages: 1 - 3
      Abstract: No abstract is available for this article.
      PubDate: 2016-12-14T23:15:39.084732-05:
      DOI: 10.1002/sta4.117
  • Inferring population size: extending the multiplier method to incorporate
           multiple traits with a likelihood-based approach
    • Authors: Vivian Yun Meng; Paul Gustafson
      Pages: 4 - 13
      Abstract: Estimating population size is an important task for resource planning and policy making. One method is the “multiplier method” that uses information about a binary trait to infer the size of a population. This paper presents a likelihood-based estimator that generalizes the multiplier method to accommodate multiple traits as well as any number of categories in a trait. To provide guidelines for study design, we quantify the advantage of using multiple traits (multiple multipliers) by studying the estimator's asymptotic standard deviation (ASD). Inclusion of multiple traits reduces the ASD most effectively when the traits are uncorrelated and of low prevalence (roughly less than 10%), but the amount of reduction in ASD diminishes when the number of traits becomes large. A Bayesian implementation of our method is applied to both simulated data and real data pertaining to an injection-drug user population. Copyright © 2016 John Wiley & Sons, Ltd.
      PubDate: 2016-12-09T00:35:24.919233-05:
      DOI: 10.1002/sta4.131
  • Time-varying rankings with the Bayesian Mallows model
    • Authors: Derbachew Asfaw; Valeria Vitelli, Øystein Sørensen, Elja Arjas, Arnoldo Frigessi
      Pages: 14 - 30
      Abstract: We present new statistical methodology for analysing rank data, where the rankings are allowed to vary in time. Such data arise, for example, when the assessments are based on a performance measure of the items, which varies in time, or if the criteria, according to which the items are ranked, change in time. Items can also be absent when the assessments are made, because of delayed entry or early departure, or purely randomly. In such situations, also the dimension of the rank vectors varies in time. Rank data in a time-dependent setting thus lead to challenging statistical problems. These problems are further complicated, from the perspective of computation, by the large dimension of the sample space consisting of all permutations of the items. Here, we focus on introducing and developing a Bayesian version of the Mallows rank model, suitable for situations in which the ranks vary in time and the assessments can be incomplete. The consequent missing data problems are handled by applying Bayesian data augmentation within Markov chain Monte Carlo. Our method is also adapted to the task of future rank prediction. The method is illustrated by analysing some aspects of a data set describing the academic performance, measured by a series of tests, of a class of high school students over a period of 4 years. Copyright © 2016 John Wiley & Sons, Ltd.
      PubDate: 2016-12-28T01:05:33.790261-05:
      DOI: 10.1002/sta4.132
  • Wiley-Blackwell Announces Launch of Stat – The ISI's Journal for the
           Rapid Dissemination of Statistics Research
    • PubDate: 2012-04-17T04:34:14.600281-05:
      DOI: 10.1002/sta4.1
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