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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 8)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Biometrics     Hybrid Journal   (Followers: 53)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Computational Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 36)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 24)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 40, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Journal of Forecasting     Hybrid Journal   (Followers: 20)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 74, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal  
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 40)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 27)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 34)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Review of Economics and Statistics     Hybrid Journal   (Followers: 164)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 45)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 8)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 30)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 151)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
The American Statistician     Full-text available via subscription   (Followers: 26)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Environmental and Ecological Statistics
Journal Prestige (SJR): 0.594
Citation Impact (citeScore): 1
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-3009 - ISSN (Online) 1352-8505
Published by Springer-Verlag Homepage  [2467 journals]
  • Measuring differences in efficiency in waste collection and disposal
           services from the EU targets in Campania municipalities

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      Abstract: Abstract The study analyses the economic and environmental performance of the 353 municipalities in the region of Campania in waste disposal and collection services. The study consists of three steps. Firstly, municipal performance in waste management services from a linear economy point view is assessed. Secondly, a circular economy paradigm is considered, and the economic (costs minimization) and environmental (unsorted waste minimization) performance is measured jointly. For these propose, two different Data Envelopment Analysis models are employed using the information provided by the Institute for Environmental Protection and Research for the year 2016. Third, in order to rank the most virtuous municipalities toward a circular economy paradigm, the study defines a measure of the efficiency deviation from environmental sustainability. The results show a cluster of municipalities in the metropolitan area of Naples and Caserta with a worse performance in the environmental dimension but with a good performance in the economic dimension. The succession of national and regional regulations has accentuated the uncertainty in the executive process and in the management of the waste cycle, creating a regulatory vacuum. Local governments should act on citizen motivations, promoting awareness on environmental issues, and should implement time-saving collection methods.
      PubDate: 2023-01-11
       
  • Impact of social integration and government support on ecological
           immigrants’ vulnerability to poverty

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      Abstract: Abstract This study identifies ways to help ecological immigrants adapt—socially, economically, psychologically, and culturally—to life in the resettlement location and thereby reduce the probability of their poverty or return to poverty, which is the main concern of immigrants relocating from the Qinba mountainous area of China. Using field survey data from ecological immigrant households in Southern Shaanxi Province and a Tobit model, we empirically tested the impact of the social integration indexes—social acceptance, psychological identity, economic integration, and cultural integration—on the migrants’ vulnerability to poverty. We also tested the moderating role of government support in this process. The results are as follows: First, the social integration index and its four dimensions have a significant negative effect on vulnerability to poverty—the higher the level of social integration among ecological immigrants, the lower the probability of poverty and return to poverty. Second, government support plays a significant positive moderating role in the relationship between social integration and vulnerability to poverty; that is, the effect of social integration on alleviating vulnerability to poverty increases with the level of help and subsidies provided by the government. Therefore, the government must increase vocational skills training for immigrants, regularly organize cultural and sports activities, improve psychological counselling provision, and improve social integration among ecological immigrants, to reduce their vulnerability to poverty.
      PubDate: 2023-01-09
       
  • The effect of financial development and economic growth on ecological
           footprint in Azerbaijan: an ARDL bound test approach with structural
           breaks

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      Abstract: Abstract Is it possible to protect the environment while aiming at economic growth, one of the most critical factors in increasing the welfare of societies and individuals, and financial development, which is also essential for economic growth' Our study addresses this question for the example of Azerbaijan, using the ARDL bound test with structural breaks over the period from 1996 to 2017. Our study aims to contribute to the growing literature body investigating the relationship between economics and the environment by: (i) using ecological footprint as an indicator in the examination of the effect of financial development and economic growth on the environment, (ii) investigating the relationship between the related variables with the structural break econometric method that can produce results that vary over time, (iii) carrying out the study for Azerbaijan. While the study results showed an inverted U-shaped environmental Kuznets curve between economic growth and ecological footprint, it was concluded that financial development also reduced the ecological footprint. When evaluated in this context, it is emphasized that while targeting economic and financial development, public authorities, financial institutions, producers, and individuals should act with a pro-environmental consciousness in the production, consumption, and decision-making processes.
      PubDate: 2023-01-07
       
  • The impacts of migrants on environmental degradation in developing
           countries

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      Abstract: Abstract The prolonged issue of environmental degradation, especially in developing countries, has urgently called for a solution by first identifying the source of the problem. Poverty has been identified as among the core cause of environmental degradation. But we also foresee the prospect of migrations from poor countries as an additional force that leads to the worsening of environmental deterioration. Empirically, this paper investigates the effect of migration on the environmental deterioration of 29 developing countries for the period between 1980 and 2019. Adopting the panel cointegration approach, the paper finds evidence that deterioration seems to be higher in countries with a higher level of migration. Although the results could be undesirable to the host countries, the best and win–win solution could be achieved by the government of the host countries, either with or without the assistance of the United Nations, to introduce more assistance to support their life and educate their citizens to be more environmentally savvy.
      PubDate: 2023-01-06
       
  • Nonparametric conditional density estimation in a deep learning framework
           for short-term forecasting

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      Abstract: Abstract Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution estimator for the purposes of forecasting tropical cyclone intensity. Many machine learning techniques give a single-point prediction of the conditional distribution of the target variable, which does not give a full accounting of the prediction variability. Conditional distribution estimation can provide extra insight on predicted response behavior, which could influence decision-making and policy. We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated. A smooth model is fit over both the target variable and covariates, and a logistic transformation is applied on the model output layer to produce an expression of the conditional density function. We provide two examples of machine learning models that can be used, polynomial regression and deep learning models. To achieve computational efficiency, we propose a case–control sampling approximation to the conditional distribution. A simulation study for four different data distributions highlights the effectiveness of our method compared to other machine learning-based conditional distribution estimation techniques. We then demonstrate the utility of our approach for forecasting purposes using tropical cyclone data from the Atlantic Seaboard. This paper gives a proof of concept for the promise of our method, further computational developments can fully unlock its insights in more complex forecasting and other applications.
      PubDate: 2022-12-01
      DOI: 10.1007/s10651-021-00499-z
       
  • Effects of choice of baseline on the uncertainty of population and
           biodiversity indices

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      Abstract: Abstract Many monitoring programs provide annual indices of relative change over time in some quantitative measure of ecological status, such as population abundance or species richness. These indices are usually scaled relative to a reference year so that they represent change in ecological status compared to this particular year. An issue with this approach is that uncertainty about ecological status in the reference year can propagate into large uncertainty in all other index values. Taking instead the mean of the ecological status over several years as the reference—a reference period—may reduce uncertainty in indices. At present, this approach is not commonly used in practice. I quantitatively evaluate how the choice of reference period affects the uncertainty of two variants of population indices, either estimated independently each year or smoothed over several years, for 100 bird species using monitoring data. Short reference periods containing years early in the series lead to reduced uncertainty in independently estimated index values, but not in smoothed indices, compared to when using a single reference year. When a long reference period was used, uncertainty was substantially reduced for independently estimated annual indices in particular, but also for smoothed indices. An exception to the reduction in uncertainty with the length of the reference period was found when indices are constrained to be log-linear. Given an appropriate model and indices that are not strictly log-linear, using smoothing and/or reference the periods can be useful ways of reducing irrelevant uncertainty in the presentation of indices.
      PubDate: 2022-11-21
       
  • Correction to: Exploring land use determinants in Italian municipalities:
           comparison of spatial econometric models

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      PubDate: 2022-10-14
      DOI: 10.1007/s10651-022-00546-3
       
  • Free-ranging dogs’ lifetime estimated by an approach for long-term
           survival data with dependent censoring

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      Abstract: Abstract Populations of free-ranging dogs are still a matter of concern in developing countries. The presence of stray dogs is associated with environmental and public health consequences such as the spread of zoonotic diseases. Therefore, public health managers base the promotion of public health on sanitary measures, including the control of the free-ranging dogs’ population. In this context, it is necessary to evaluate the free-ranging dogs’ life dynamics, taking into account all characteristics of the data, including long-term survival. In long-term studies, some causes of censoring are generally falsely assumed to be independent, leading to bias neglected. Therefore, we propose a likelihood-based approach for long-term clustered survival data, which is suitable to accommodate the dependent censoring. The association between lifetimes and dependent censoring is accommodated through the conditional approach of the frailty models. The marginal distributions can be adjusted assuming Weibull or piecewise exponential distributions, respectively. A Monte Carlo Expectation–Maximization algorithm is developed to estimate the proposed estimators. The simulation study results show a small relative bias and coverage probability near to the nominal level, indicating that the proposed approach works well. Moreover, the model identifiability is assured once data has a cluster structure. Finally, we analyze the survival times of free-ranging dogs from the West Bengal, India, collected between 2010 to 2015, and conclude that survival time (death due to natural cause) is negatively correlated to dependent censoring (missing cause).
      PubDate: 2022-09-30
      DOI: 10.1007/s10651-022-00549-0
       
  • Testing environmental effects on taxonomic composition with canonical
           correspondence analysis: alternative permutation tests are not equal

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      Abstract: Abstract After applying canonical correspondence analysis to metagenomics data with hugely different library sizes (site totals) it became evident that Canoco and the R-packages ade4 and vegan can yield (at least up to 2022) very different P-values in statistical tests of the relationship between taxonomic composition (species composition) and predictors (environmental variables and/or treatments). The reason is that vegan and Canoco up to version 5.12 apply residualized response permutation (but ignore the model intercept), whereas ade4 applies predictor permutation. Predictor permutation, when extended to residualized predictor permutation, is applicable in partial constrained ordination. This paper shows by simulation that residualized response permutation can yield a very inflated Type I error rate, if the abundance data are both overdispersed and highly variable in site total. In contrast, residualized predictor permutation controlled the type I error rate and had good power, also when the predictors were skewed or binary. After square-root or log transformation of the abundance data, the differences between the permutation methods became small. Residualized predictor permutation is recommended, particularly in testing trait–environment relationships using double constrained correspondence analysis, because this method also critically depends on the species totals, which are generally highly variable. It is implemented in Canoco 5.15 and the R-code of this paper.
      PubDate: 2022-09-13
      DOI: 10.1007/s10651-022-00545-4
       
  • Some efficient closed-form estimators of the parameters of the generalized
           Pareto distribution

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      Abstract: Abstract In this paper, we consider several families of closed-form estimators of the two parameters of the Generalized Pareto Distribution (GPD). These estimators are easy to compute and have high efficiency when compared to previously proposed methods. We also consider some estimators which are not of closed-form. All methods are based on certain order statistics. The proposed procedures are best for extreme values of the shape parameters and sample sizes of 100 or larger. Monte Carlo simulations are conducted to investigate the performance of the proposed parameter estimation procedures. Our findings suggest that the proposed estimation methods are competitive compared to the existing methods. We provide a real data application to illustrate the utilization of the proposed methods in estimating the GPD parameters.
      PubDate: 2022-09-12
      DOI: 10.1007/s10651-022-00548-1
       
  • Assessing the effects of multivariate functional outlier identification
           and sample robustification on identifying critical PM2.5 air pollution
           episodes in Medellín, Colombia

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      Abstract: Abstract Identification of critical episodes of environmental pollution, both as a outlier identification problem and as a classification problem, is a usual application of multivariate functional data analysis. This article addresses the effects of robustifying multivariate functional samples on the identification of critical pollution episodes in Medellín, Colombia. To do so, it compares 18 depth-based outlier identification methods and highlights the best options in terms of precision through simulation. It then applies the two methods with the best performance to robustify a real dataset of air pollution (PM2.5 concentration) in the Metropolitan Area of Medellín, Colombia and compares the effects of robustifying the samples on the accuracy of supervised classification through the multivariate functional DD-classifier. Our results show that 10 out of 20 methods revised perform better in at least one kind outliers. Nevertheless, no clear positive effects of robustification were identified with the real dataset.
      PubDate: 2022-09-07
      DOI: 10.1007/s10651-022-00544-5
       
  • Fast estimation and choice of confidence interval methods for step
           regression

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      Abstract: Abstract In this paper we propose a new fast grid search algorithm for finding the least square estimators of a step regression model. This algorithm makes it practical to compute resampling-based confidence intervals for step regression models. We introduce five data generating models, including one where the mean model is a step model (model correctly specified) and four where the mean models are not step models (model misspecified), and use them to study the coverage probabilities of two new types of resampling-based confidence intervals for step regression: symmetric percentile bootstrap confidence intervals and subsampling confidence intervals using a new set of rules-of-thumb to select block size. Our results show that when the model is correctly specified, the symmetric percentile Efron bootstrap confidence intervals provide close-to-nominal coverage and have shorter intervals than the subsampling methods; when the model is misspecified, the subsampling method using the rules-of-thumb provides good coverage and shorter confidence intervals than the symmetric percentile Efron bootstrap method and the subsampling method using a double bootstrap-like procedure for block size selection. Finally, we apply the proposed methods to a real world environmental dataset on the relationship between grassland productivity, soil moisture anomalies and other hydro-climatic and land use variables to provide inference for the threshold in soil moisture anomalies, across which there is a jump in grassland productivity.
      PubDate: 2022-09-06
      DOI: 10.1007/s10651-022-00547-2
       
  • Bayesian multi-species N-mixture models for unmarked animal communities

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      Abstract: Abstract We propose an extension of the N-mixture model that enables the estimation of abundances of multiple species as well as the correlations between them. Our novel multi-species N-mixture model (MNM) is the first to address the estimation of both positive and negative inter-species correlations, which allows us to assess the influence of the abundance of one species on another. We provide extensions that permit the analysis of data with excess of zero counts, and relax the assumption that populations are closed through the incorporation of an autoregressive term in the abundance. Our approach provides a method of quantifying the strength of association between species’ population sizes and is of practical use to population and conservation ecologists. We evaluate the performance of the proposed models through simulation experiments in order to examine the accuracy of both model estimates and coverage rates. The results show that the MNM models produce accurate estimates of abundance, inter-species correlations and detection probabilities at a range of sample sizes. The MNM models are applied to avian point data collected as part of the North American Breeding Bird Survey between 2010 and 2019. The results reveal an increase in Bald Eagle abundance in south-eastern Alaska in the decade examined.
      PubDate: 2022-09-05
      DOI: 10.1007/s10651-022-00542-7
       
  • Correction: Nonparametric conditional density estimation in a deep
           learning framework for short-term forecasting

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      PubDate: 2022-08-26
      DOI: 10.1007/s10651-022-00543-6
       
  • Exploring land use determinants in Italian municipalities: comparison of
           spatial econometric models

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      Abstract: Abstract This study sets up a spatial econometric framework to explore the factors that best describe land consumption in Italy at the municipal level. By modelling the different types of spatial interactions and geographical proximity between all Italian municipalities, the direct effects of land use drivers are assessed together with spillover effects. Land use data are drawn from the ISPRA-SNPA 82/18 Report and cover all 7,998 Italian municipalities. The results highlight the existence of endogenous and exogenous interaction effects and the crucial role of the demographic, socio-economic and institutional structure on land use intensity. Hence the need for a planning policy aimed at: i) strengthening institutional cooperation to deal with excessive administrative fragmentation; ii) improving institutional and governmental quality to trigger virtuous mechanisms for sustainable land use management.
      PubDate: 2022-07-17
      DOI: 10.1007/s10651-022-00541-8
       
  • Evaluation of ecological security for the Association of Southeast Asian
           Nations-5 countries: new evidence from the RALS unit root test

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      Abstract: Abstract The present study seeks to determine the convergence of the ecological footprint pressure index for the Association of Southeast Asian Nations (ASEAN-5) countries over the period of 1961–2017. For this purpose, traditional unit root tests in conjunction with residual augmented least squares (RALS) type unit root tests have been applied to examine the convergence of all countries under investigation. RALS type tests were chosen due to showing a significantly improved power over conventional tests that do not use information on non-normal errors. The traditional unit root results do not show support for the ecological footprint pressure index convergence of the highlighted ASEAN-5 blocs. However, the RALS type and nonlinear unit root tests results reveal that the ecological footprint pressure index became convergent. Thus, governments and policymakers need to adopt stricter policies to protect the environment. These results have a more far-reaching effect on energy and environmental security for the study region.
      PubDate: 2022-07-14
      DOI: 10.1007/s10651-022-00540-9
       
  • Distribution-free changepoint detection tests based on the breaking of
           records

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      Abstract: Abstract The analysis of record-breaking events is of interest in fields such as climatology, hydrology or anthropology. In connection with the record occurrence, we propose three distribution-free statistics for the changepoint detection problem. They are CUSUM-type statistics based on the upper and/or lower record indicators observed in a series. Using a version of the functional central limit theorem, we show that the CUSUM-type statistics are asymptotically Kolmogorov distributed. The main results under the null hypothesis are based on series of independent and identically distributed random variables, but a statistic to deal with series with seasonal component and serial correlation is also proposed. A Monte Carlo study of size, power and changepoint estimate has been performed. Finally, the methods are illustrated by analyzing the time series of temperatures at Madrid, Spain. The R package RecordTest publicly available on CRAN implements the proposed methods.
      PubDate: 2022-07-06
      DOI: 10.1007/s10651-022-00539-2
       
  • New methods of life expectancy estimation

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      Abstract: Abstract Two novel methods of life expectancy estimation, applied to various annual reported demographic datasets, are proposed. First, for datasets that fully recorded birth date and death date of all dead individuals, we rely on the well-known Kaplan–Meier estimation method to provide an accurate estimation framework of life expectancy. Our proposed method can be used as a gold standard in the accuracy investigation of other life expectancy estimation methods. The method can be applied for small areas, where complete mortality data are regularly produced by routine annual surveys. The second new created method, called as local parametric method, based on the theoretical background of survival process with local parametric Weibull distributions, estimates life expectancy using abridged survival data. Experiments on real longitudinal datasets show the new method provides very exact life expectancy estimations for 10 among 15 one-year datasets, whilst the method of Chiang often yields overestimations.
      PubDate: 2022-05-13
      DOI: 10.1007/s10651-022-00536-5
       
  • Modeling Dinophysis in Western Andalucía using a autoregressive
           hidden Markov model

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      Abstract: Abstract Dinophysis spp. can produce diarrhetic shellfish toxins (DST) including okadaic acid and dinophysistoxins, and some strains can also produce non-diarrheic pectenotoxins. Although DSTs are of human health concern and have motivated environmental monitoring programs in many locations, these monitoring programs often have temporal data gaps (e.g., days without measurements). This paper presents a model for the historical time-series, on a daily basis, of DST-producing toxigenic Dinophysis in 8 monitored locations in western Andalucía over 2015–2020, incorporating measurements of algae counts and DST levels. We fitted a bivariate hidden Markov Model (HMM) incorporating an autoregressive correlation among the observed DST measurements to account for environmental persistence of DST. We then reconstruct the maximum-likelihood profile of algae presence in the water column at daily intervals using the Viterbi algorithm. Using historical monitoring data from Andalucía, the model estimated that potentially toxigenic Dinophysis algae is present at greater than or equal to 250 cells/L between< 1% and>10% of the year depending on the site and year. The historical time-series reconstruction enabled by this method may facilitate future investigations into temporal dynamics of toxigenic Dinophysis blooms.
      PubDate: 2022-05-04
      DOI: 10.1007/s10651-022-00534-7
       
  • Inference and model determination for temperature-driven non-linear
           ecological models

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      Abstract: Abstract This paper is concerned with a contemporary Bayesian approach to the effect of temperature on developmental rates. We develop statistical methods using recent computational tools to model four commonly used ecological non-linear mathematical curves that describe arthropods’ developmental rates. Such models address the effect of temperature fluctuations on the developmental rate of arthropods. In addition to the widely used Gaussian distributional assumption, we also explore Inverse Gamma-based alternatives, which naturally accommodate adaptive variance fluctuation with temperature. Moreover, to overcome the associated parameter indeterminacy in the case of no development, we suggest the zero-inflated Inverse Gamma model. The ecological models are compared graphically via posterior predictive plots and quantitatively via marginal likelihood estimates and Information criteria. Inference is performed using the Stan software and we investigate the statistical and computational efficiency of its Hamiltonian Monte Carlo and Variational Inference methods. We also explore model uncertainty and employ Bayesian Model Averaging framework for robust estimation of the key ecological parameters.
      PubDate: 2022-03-19
      DOI: 10.1007/s10651-022-00531-w
       
 
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