Subjects -> STATISTICS (Total: 130 journals)
 Showing 1 - 151 of 151 Journals sorted alphabetically Advances in Complex Systems       (Followers: 10) Advances in Data Analysis and Classification       (Followers: 61) Annals of Applied Statistics       (Followers: 39) Applied Categorical Structures       (Followers: 4) Argumentation et analyse du discours       (Followers: 10) Asian Journal of Mathematics & Statistics       (Followers: 8) AStA Advances in Statistical Analysis       (Followers: 4) Australian & New Zealand Journal of Statistics       (Followers: 13) Bernoulli       (Followers: 9) Biometrical Journal       (Followers: 10) Biometrics       (Followers: 51) British Journal of Mathematical and Statistical Psychology       (Followers: 18) Building Simulation       (Followers: 1) Bulletin of Statistics       (Followers: 4) CHANCE       (Followers: 5) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics - Theory and Methods       (Followers: 11) Computational Statistics       (Followers: 14) Computational Statistics & Data Analysis       (Followers: 37) Current Research in Biostatistics       (Followers: 8) Decisions in Economics and Finance       (Followers: 11) Demographic Research       (Followers: 16) Electronic Journal of Statistics       (Followers: 8) Engineering With Computers       (Followers: 5) Environmental and Ecological Statistics       (Followers: 7) ESAIM: Probability and Statistics       (Followers: 5) Extremes       (Followers: 2) Fuzzy Optimization and Decision Making       (Followers: 8) Geneva Papers on Risk and Insurance - Issues and Practice       (Followers: 13) Handbook of Numerical Analysis       (Followers: 5) Handbook of Statistics       (Followers: 7) IEA World Energy Statistics and Balances -       (Followers: 2) International Journal of Computational Economics and Econometrics       (Followers: 6) International Journal of Quality, Statistics, and Reliability       (Followers: 17) International Journal of Stochastic Analysis       (Followers: 3) International Statistical Review       (Followers: 12) International Trade by Commodity Statistics - Statistiques du commerce international par produit Journal of Algebraic Combinatorics       (Followers: 4) Journal of Applied Statistics       (Followers: 20) Journal of Biopharmaceutical Statistics       (Followers: 20) Journal of Business & Economic Statistics       (Followers: 39, SJR: 3.664, CiteScore: 2) Journal of Combinatorial Optimization       (Followers: 7) Journal of Computational & Graphical Statistics       (Followers: 20) Journal of Econometrics       (Followers: 82) Journal of Educational and Behavioral Statistics       (Followers: 6) Journal of Forecasting       (Followers: 17) Journal of Global Optimization       (Followers: 7) Journal of Interactive Marketing       (Followers: 10) Journal of Mathematics and Statistics       (Followers: 8) Journal of Nonparametric Statistics       (Followers: 6) Journal of Probability and Statistics       (Followers: 10) Journal of Risk and Uncertainty       (Followers: 32) Journal of Statistical and Econometric Methods       (Followers: 5) Journal of Statistical Physics       (Followers: 13) Journal of Statistical Planning and Inference       (Followers: 8) Journal of Statistical Software       (Followers: 20, SJR: 13.802, CiteScore: 16) Journal of the American Statistical Association       (Followers: 72, SJR: 3.746, CiteScore: 2) Journal of the Korean Statistical Society       (Followers: 1) Journal of the Royal Statistical Society Series C (Applied Statistics)       (Followers: 31) Journal of the Royal Statistical Society, Series A (Statistics in Society)       (Followers: 26) Journal of the Royal Statistical Society, Series B (Statistical Methodology)       (Followers: 43) Journal of Theoretical Probability       (Followers: 3) Journal of Time Series Analysis       (Followers: 16) Journal of Urbanism: International Research on Placemaking and Urban Sustainability       (Followers: 30) Law, Probability and Risk       (Followers: 8) Lifetime Data Analysis       (Followers: 7) Mathematical Methods of Statistics       (Followers: 4) Measurement Interdisciplinary Research and Perspectives       (Followers: 1) Metrika       (Followers: 4) Modelling of Mechanical Systems       (Followers: 1) Monte Carlo Methods and Applications       (Followers: 6) Monthly Statistics of International Trade - Statistiques mensuelles du commerce international       (Followers: 2) Multivariate Behavioral Research       (Followers: 5) Optimization Letters       (Followers: 2) Optimization Methods and Software       (Followers: 8) Oxford Bulletin of Economics and Statistics       (Followers: 34) Pharmaceutical Statistics       (Followers: 17) Probability Surveys       (Followers: 4) Queueing Systems       (Followers: 7) Research Synthesis Methods       (Followers: 7) Review of Economics and Statistics       (Followers: 124) Review of Socionetwork Strategies Risk Management       (Followers: 15) Sankhya A       (Followers: 2) Scandinavian Journal of Statistics       (Followers: 9) Sequential Analysis: Design Methods and Applications Significance       (Followers: 7) Sociological Methods & Research       (Followers: 37) SourceOCDE Comptes nationaux et Statistiques retrospectives SourceOCDE Statistiques : Sources et methodes SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques       (Followers: 1) SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance       (Followers: 2) SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques       (Followers: 1) SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques SourceOECD Monthly Statistics of International Trade       (Followers: 1) SourceOECD National Accounts & Historical Statistics SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE       (Followers: 2) SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie SourceOECD Statistics Sources & Methods       (Followers: 1) SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires Stata Journal       (Followers: 9) Statistica Neerlandica       (Followers: 1) Statistical Applications in Genetics and Molecular Biology       (Followers: 5) Statistical Communications in Infectious Diseases Statistical Inference for Stochastic Processes       (Followers: 3) Statistical Methodology       (Followers: 7) Statistical Methods and Applications       (Followers: 6) Statistical Methods in Medical Research       (Followers: 27) Statistical Modelling       (Followers: 19) Statistical Papers       (Followers: 4) Statistical Science       (Followers: 13) Statistics & Probability Letters       (Followers: 13) Statistics & Risk Modeling       (Followers: 2) Statistics and Computing       (Followers: 13) Statistics and Economics       (Followers: 1) Statistics in Medicine       (Followers: 193) Statistics, Politics and Policy       (Followers: 6) Statistics: A Journal of Theoretical and Applied Statistics       (Followers: 14) Stochastic Models       (Followers: 3) Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports       (Followers: 2) Structural and Multidisciplinary Optimization       (Followers: 12) Teaching Statistics       (Followers: 7) Technology Innovations in Statistics Education (TISE)       (Followers: 2) TEST       (Followers: 3) The American Statistician       (Followers: 24) The Annals of Applied Probability       (Followers: 8) The Annals of Probability       (Followers: 10) The Annals of Statistics       (Followers: 34) The Canadian Journal of Statistics / La Revue Canadienne de Statistique       (Followers: 11) Wiley Interdisciplinary Reviews - Computational Statistics       (Followers: 1)
Similar Journals
 Environmental and Ecological StatisticsJournal Prestige (SJR): 0.594 Citation Impact (citeScore): 1Number of Followers: 7      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-3009 - ISSN (Online) 1352-8505 Published by Springer-Verlag  [2626 journals]
• A constrained-memory stress release model (CM-SRM) for the earthquake
occurrence in the Corinth Gulf (Greece)
• Abstract: Abstract The complexity of seismogenesis requires the development of stochastic models, the application of which aims to improve our understanding on the seismic process and the associated underlying mechanisms. Seismogenesis in the Corinth Gulf (Greece) is modeled through a Constrained-Memory Stress Release Model (CM-SRM), which combines the gradual increase of the strain energy due to continuous slow tectonic strain loading and its sudden release during an earthquake occurrence. The data are treated as a point process, which is uniquely defined by the associated conditional intensity function. In the original form of the Simple Stress Release Model (SSRM), the conditional intensity function depends on the entire history of the process. In an attempt to identify the most appropriate parameterization that better fits the data and describes the earthquake generation process, we introduce a constrained “ $$m$$ -memory” point process, implying that only the $$m$$ most recent arrival times are taken into account in the conditional intensity function, for some suitable $$m \in N$$ . Modeling of this process is performed for moderate earthquakes (M ≥ 5.2) occurring in the Corinth Gulf since 1911, by considering in each investigation different number of steps backward in time. The derived model versions are compared with the SSRM in its original form and evaluated in terms of information criteria and residual analysis.
PubDate: 2021-01-10

• Measuring the impact of higher education on environmental pollution: new
evidence from thirty provinces in China
• Abstract: Abstract The study reported in this article investigated the relationship between higher education and environmental sustainability with control variables including foreign direct investment, electricity consumption, population, and gross domestic product from 30 provinces in China during the 2000–2018 period. The data were analyzed with cross-sectional dependency tests, panel unit-root tests, Kao cointegration tests, fully modified ordinary least squares, and dynamic ordinary least squares. Some of the main results are presented as follows. First, the results showed that higher education and foreign direct investment play a vital role in mitigating CO2 emissions, thereby confirming both the education-CO2 led hypothesis and the pollution halo hypothesis, respectively. Second, the estimates suggested that an increase in electricity consumption, population, and gross domestic product significantly contributed to enhancements in CO2 emissions. Based on the current estimated results, this research proposes important policies to help policymakers and governments in mitigating CO2 emissions.
PubDate: 2021-01-10

• Neyman–Scott process with alpha-skew-normal clusters
• Abstract: Abstract The Neyman–Scott processes introduced so far assume a symmetric distribution for the positions of the offspring points and this makes them inappropriate for modelling the skewed and bimodal clustered patterns and is a hindrance in fitting them to data that exhibit skewness or bimodality. In this paper, we apply the bivariate alpha-skew-normal distribution to the locations of the offspring points and introduce a Neyman–Scott process that regulates skewness and bimodality shapes in clustered point patterns. For this process, we obtain closed forms of the pair correlation function and the third-order intensity reweighted product density function and by use of the composite likelihood method, we fit the model to data. To examine the goodness-of-fit of the presented model, we use a statistical test based on the combined global scaled MAD envelopes. The use of the introduced process to model a clustered point pattern is illustrated by application to the locations of a species of trees in a rainforest dataset.
PubDate: 2021-01-08

• Analysing the relationship between district heating demand and weather
conditions through conditional mixture copula
• Abstract: Abstract Efficient energy production and distribution systems are urgently needed to reduce world climate change. Since modern district heating systems are sustainable energy distribution services that exploit renewable sources and avoid energy waste, in-depth knowledge of thermal energy demand, which is mainly affected by weather conditions, is essential to enhance heat production schedules. We hence propose a mixture copula-based approach to investigate the complex relationship between meteorological variables, such as outdoor temperature and solar radiation, and thermal energy demand in the district heating system of the Italian city Bozen-Bolzano. We analyse data collected from 2014 to 2017, and estimate copulas after removing serial dependence in each time series using autoregressive integrated moving average models. Due to complex relationships, a mixture of an unstructured Student-t and a flipped Clayton copula is deemed the best model, as it allows differentiating the magnitude of dependence in each tail and exhibiting both heavy-tailed and asymmetric dependence. We derive the conditional copula-based probability function of thermal energy demand given meteorological variables, and provide useful insight on the production management phase of local energy utilities.
PubDate: 2021-01-06

• Assessing competition among species through simultaneously modeling
marginal counts and respective proportions
• Abstract: Abstract Evolution processes of multiple competitive and non-competitive species have traditionally been handled using different methods. In particular, evolution processes of multiple competitive species have usually been evaluated by the continuous and discrete proportions analysis; however, such evolution processes cannot be solely characterized by their relative proportions in practice. In this paper, we introduce a community based Poisson model with multivariate random effects to explicitly characterize marginal counts and respective proportions simultaneously. Furthermore, our method provides a unified approach to handle evolution processes of competitive and non-competitive species. In fact, the existence and strength of the competition among species can be assessed through our approach. Unlike those marginal modelling methods, our approach explicitly predicts random effects. Our model inference does not rely on distributional assumption of observed multivariate random effects, and thus is more robust than traditional approaches assuming parametric random effects.
PubDate: 2021-01-02

• Computationally simple anisotropic lattice covariograms
• Abstract: Abstract When working with contemporary spatial ecological datasets, statistical modellers are often confronted by two major challenges: (I) the need for covariance models with the flexibility to accommodate directional patterns of anisotropy; and (II) the computational effort demanded by high-dimensional inverse and determinant problems involving the covariance matrix $$\vec {V}$$ . In the case of rectangular lattice data, the spatially separable covariogram is a longstanding but underused model that can reduce arithmetic complexity by orders of magnitude. We examine a class of covariograms for stationary data that extends the separable model through affine coordinate transformations, providing a far greater flexibility for handling anisotropy than that offered by the standard approach of using geometric anisotropy to extend an isotropic model. This motivates our development of an extremely fast estimator of the orientation of the axes of range anisotropy on spatial lattice data, and a powerful visual diagnostic for nonstationarity. In a case study, we demonstrate how these tools can be used to analyze and predict forest damage patterns caused by outbreaks of the mountain pine beetle.
PubDate: 2020-12-01

• Cluster analysis methods applied to daily vessel location data to identify
cooperative fishing among tuna purse-seiners
• Abstract: Abstract Management of large-scale pelagic fisheries relies heavily on fishery data to provide information on tuna population status because, for widely distributed populations, the cost of collecting survey data is often prohibitively high. However, fishery data typically do not provide direct information on interactions among fishing vessels, and thus methods of analysis often assume that vessels operate independently, despite the belief that cooperative fishing occurs. Cluster analysis methods were applied to daily vessel location data collected by onboard fisheries observers to identify groups of tuna purse-seine vessels searching for fish close to each other in space. Some vessel groups were found to reoccur through time, both on daily and monthly or longer time scales. This temporal persistence and reoccurrence are interpreted as an indication of cooperative fishing. Results indicate that there may be multiple layers of vessel interactions, from groups of a few vessels to networks of larger numbers of vessels. The use of reoccurring vessel group characteristics to study the temporal and spatial persistence of areas of high tuna abundance is discussed.
PubDate: 2020-12-01

• Improved prediction for a spatio-temporal model
• Abstract: Abstract We investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions using only the expectation given by the model. Further, we showed that these improvements in predictive accuracy were maintained even when using a reduced subset of the data for generating predictions.
PubDate: 2020-12-01

• Comparing methods to estimate the proportion of turbine-induced bird and
bat mortality in the search area under a road and pad search protocol
• Abstract: Abstract Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance, and ideally also for variation with direction (anisotropy). We compare five methods of accounting for carcasses that land outside the searched area (ratio, weighted distribution, non-parametric, and two generalized linear models (glm)) by simulating spatial arrival patterns and the detection process to mimic observations which result from surveying only, or primarily, roads and pads (R&P) and applying the five methods. Simulations vary R&P configurations, spatial carcass distributions (isotropic and anisotropic), and per turbine fatality rates. Our results suggest that the ratio method is less accurate with higher variation relative to the other four methods which all perform similarly under isotropy. All methods were biased under anisotropy; however, including direction covariates in the glm method substantially reduced bias. In addition to comparing methods of accounting for unsearched areas, we suggest a semiparametric bootstrap to produce confidence-based bounds for the proportion of carcasses that land in the searched area.
PubDate: 2020-11-30

• Special Issue: Statistical mathematics for ecological and environmental
data
• PubDate: 2020-11-17

• Marked spatio-temporal point patterns, periodicity analysis and
earthquakes: an analytical extension including hypocenter depth
• Abstract: Abstract The longitude, latitude and depth of the hypocenter in 3-D space and the date and time of rupture makes an earthquake a “point” in a spatio-temporal point pattern, observed over a region and months, years or decades. The magnitude of earthquakes marks the point pattern, as would hypocenter depth do if only the longitude and latitude of epicenters were used for location in 2-D space. Stochastic declustering, based on a space–time ETAS model (ETAS: epidemic-type aftershock sequence), is a procedure that can be applied in the preliminary stage of an earthquake catalog data analysis. Stochastic declustering procedures have underlying assumptions, such as the time independence of the background intensity function whether the spatial framework is 2-D or 3-D, and a separate treatment of hypocenter depth from longitude and latitude when the spatial framework is 3-D. Cyclical processes in the Earth, including tides and seasonal surface water loads, can introduce periodic behavior in earthquake occurrence and related variables. The effects of ETAS-based 2-D and 3-D declustering on the outcome of periodicity analyses performed from the resulting earthquake data catalogs are studied. The research objectives and statistical challenges include the detection of periodicities for hypocenter depth in addition to monthly earthquake number, and the risk of missing observations for hypocenter depth when the monthly earthquake number after declustering is zero. A version of the method of multi-frequential periodogram analysis (MFPA) that allows for missing observations in the input temporal series is presented in detail, and applied to hypocenter depth (monthly mean and median) for central and northern California from January 2006 to December 2014. The results obtained for the 2-D and 3-D declustered catalogs are compared with those for the original catalog for this region. A semiannual periodicity in hypocenter depth is detected for the original and 2-D declustered catalogs, and fitted with the goal of relating it to periodicities found in the time series of monthly earthquake numbers. Using these results for central and northern California earthquakes, some of the assumptions on the intensity function of the spatio-temporal point process in stochastic declustering are discussed and future research perspectives are proposed.
PubDate: 2020-11-09

• Bayesian predictive model selection in circular random effects models with
applications in ecological and environmental studies
• Abstract: Abstract In this paper we present a detailed comparison of the prediction error based model selection criteria in circular random effects models. The study is primarily motivated by the need for an understanding of their performance in real life ecological and environmental applications. Prediction errors are based on posterior predictive distributions and the model selection methods are adjusted for the circular manifold. Plug-in estimators of the circular distance parameters are also considered. A Monte Carlo experiment scheme taking the account of various realistic ecological and biological scenarios is designed. We introduced a coefficient that is based on conditional expectations to examine how the deviation from von Mises (vM) distribution, the standard choice in applications, effects the performances. Our results show that the performances of widely used circular predictive model selection criteria mostly depend on the sample size as well as within-sample-correlation. The approaches and selection strategies are then applied to investigate orientational behaviour of Talitrus saltator under the risk of dehydration and direction of wind with respect to associated atmoshperic variables.
PubDate: 2020-10-26

• Financial development, globalization and ecological footprint in G7:
further evidence from threshold cointegration and fractional frequency
causality tests
• Abstract: Abstract This paper empirically explores the dynamic relationships between financial development, globalization, energy consumption, economic growth, and ecological footprint in G7 countries over the period 1980–2015. Using a recently introduced threshold cointegration test with an endogenous structural break, the paper aims primarily to determine the effects of financial development and globalization on environmental degradation. The results confirm the presence of cointegration in Canada, Italy, and Japan. The long-run estimates indicate that globalization significantly reduces ecological footprint in Canada and Italy, while financial development reduces pollution in Japan. The findings also demonstrate that energy consumption stimulates environmental degradation in these three countries. Furthermore, the causality test that considers smooth structural breaks via a fractional frequency flexible Fourier function indicates that globalization causes ecological footprint in all the G7 countries except France, while financial development causes ecological footprint in France, Japan, and the United Kingdom. Finally, the overall results suggest that globalization is a more effective tool than financial development in regulating ecological footprint for G7 countries. Therefore, we recommend that policymakers should make use of the opportunities that globalization offers to solve environmental problems.
PubDate: 2020-10-26

• Estimating historic movement of a climatological variable from a pair of
misaligned functional data sets
• Abstract: Abstract We consider the problem of estimating the mean function from a pair of paleoclimatic functional data sets after one of them has been registered with the other. We establish that registering one data set with respect to the other is the appropriate way to formulate this problem. This is in contrast with estimation of the mean function on a ‘central’ time scale that is preferred in the analysis of multiple sets of longitudinal growth data. We show that if a consistent estimator of the time transformation is used for registration, the Nadaraya–Watson estimator of the mean function based on the registered data would be consistent under a few additional conditions. We study the potential change in asymptotic mean squared error of the estimator because of the contribution of the time-transformed data set. We demonstrate through simulations that the additional data can lead to improved estimation despite estimation error in registration. Analysis of three pairs of paleoclimatic data sets reveals some interesting points.
PubDate: 2020-10-15

• Forecasting the Yellow River runoff based on functional data analysis
methods
• Abstract: Abstract This study examines the runoff prediction of each hydrometric station and each month in the mainstream of the Yellow River in China. From the perspective of functional data, the monthly runoff of each hydrometric station can be regarded as a function of both time and space. A sequence of such functions is formed by collecting the data over the years. We propose a new approach by combining the two-dimensional functional principal component analysis (FPCA) and time series analysis methods to predict the runoff. In the simulation, we compared the proposed method with two others: one based on one-dimensional FPCA and the seasonal auto-regressive integrated moving average (SARIMA) method. The method combining standard two-dimensional FPCA and time series analysis outperforms others in most cases, and is used to predict the runoff of each hydrometric station and each month in the Yellow River in 2018.
PubDate: 2020-10-10

• Spatial sampling for a rabies vaccination schedule in rural villages
• Abstract: Abstract Efforts are being made to contain rabies in Tanzania, reported in the southern highland regions, since 1954, and endemic in all districts in Tanzania currently. It has been determined that mass vaccination of at least $$70\%$$ of a domestic animal population is most effective in reducing transmission of rabies. Current vaccination campaigns in Tanzanian villages have many administrative and logistical challenges. Animals roam freely, making a full population vaccination impossible. Spatial sampling of households in villages is proposed, where optimality is measured through the distance traversed by the vaccinator by foot for vaccinating at each sampled household. The walking distance is attained by incorporating a driving network between optimally determined stopping points from which the vaccinator then walks for executing vaccinations, while ensuring the $$70\%$$ coverage of the animal population. We illustrate the sampling schemes on a real dataset using simulations. A systematic regular spatial sampling is found to be optimal. The vaccination scheme proposed, provides an effective way to manage a vaccination campaign.
PubDate: 2020-10-10

• A puzzle over ecological footprint, energy consumption and economic
growth: the case of Turkey
• Abstract: Abstract The paper investigates the non-linear causality from energy consumption and economic growth to ecological footprint for the case of Turkey by employing ARDL Models and ECM-Based Granger Causality over the period from 1961 to 2016. The major contribution of the article to the literature is that (i) the data period of the empirical analysis of the study is much longer than the one of the other studies for the case of Turkey; (ii) ecological footprint, which has been rarely used in the studies for the same case is taken as a sophisticated proxy of environmental degradation; (iii) it is found that the sophisticated key term ‘awareness’ needs much more multidisciplinary attention and wider mind maps as the causality from energy consumption to ecological footprint has U-shape; (iv) the non-linear causality is investigated and the complicated puzzle is discussed in the framework of a wide and coherent mind map.
PubDate: 2020-10-09

• A far-near sparse covariance model with application in climatology
• Abstract: Abstract Teleconnection, the strong dependence between two distant locations, provides interesting information for discovering the structures in spatial data. While teleconnections are often sparse and estimated through sample correlations, there are also abundant correlations among nearby locations. We propose a far-near covariance model that simultaneously models the abundant short-distance dependencies and the sparse long-distance dependence. This approach provides a new framework for utilizing the short-distance dependence structure to improve the exploration and estimation of the sparse remote dependence signals. The statistical properties of proposed estimators are provided. The detection of teleconnection in high-dimensional data is a multiple testing problem. We relate this detection problem to $$\tau$$ -coherence statistical testing and generalize the $$\tau$$ -coherence for the covariance matrix of two-dimensional grid locations. The applications are illustrated through numerical studies on both synthetic data and real climate data.
PubDate: 2020-09-18

• A fuzzy goal programming with interval target model and its application to
the decision problem of renewable energy planning
• Abstract: Abstract Optimizing sustainable renewable energy portfolios is one of the most complicated decision making problems in energy policy planning. This process involves meeting the decision maker’s preferences, which can be uncertain, while considering several conflicting criteria, such as environmental, societal, and economic impact. In this paper, rather than using existing techniques, a novel multi-objective decision making (MODM) model, named fuzzy goal programming with interval target (FGP-IT), is proposed and constructed based on recent developments and concepts in fuzzy goal programming (FGP) and revised multi-choice goal programming (RMCGP). The model deals with decision making problems involving a high level of uncertainty by offering decision makers a more flexible way to formulate and express their preferences, namely, fuzzy interval target goals. The proposed method is used to optimize a hypothetical sustainable wind energy portfolio in Algeria. The results show that the FGP-IT model is capable of assisting decision makers with uncertain preferences in making such complicated decisions.
PubDate: 2020-07-14

• Estimating total species using a weighted combination of expected mixture
distribution component counts
• Abstract: Abstract In this paper we present a weighted mixture distribution component counts (MDCC) approach for estimating total number of species. The proposed method combines conditional estimates of component counts from several candidate mixture distributions and uses bootstrap for confidence interval estimation. The distribution specification is flexible and can be adjusted to suit a variety of datasets. Smoothing techniques can also be incorporated to improve modeling of sparse data. The method is tested by a simulation study and applied to two microbiome datasets for illustration. Simulation results indicate improved bias, mean squared error and confidence interval coverage relative to comparison methods, as well as robustness to underlying data structure.
PubDate: 2020-06-27

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