 Subjects -> STATISTICS (Total: 130 journals)
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- Modeling the biological growth with a random logistic differential
equation-
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Abstract: We modeled biological growth using a random differential equation (RDE), where the initial condition is a random variable, and the growth rate is a suitable stochastic process. These assumptions let us obtain a model that represents well the random growth process observed in nature, where only a few individuals of the population reach the maximal size of the species, and the growth curve for every individual behaves randomly. Since we assumed that the initial condition is a random variable, we assigned a priori density, and we performed Bayesian inference to update the initial condition’s density of the RDE. The Karhunen–Loeve expansion was then used to approximate the random coefficient of the RDE. Then, using the RDE’s approximations, we estimated the density f(p, t). Finally, we fitted this model to the biological growth of the giant electric ray (or Cortez electric ray) Narcine entemedor. Simulations of the solution of the random logistic equation were performed to construct a curve that describes the solutions’ mean for each time. As a result, we estimated confidence intervals for the mean growth that described reasonably well the observed data. We fit the proposed model with a training dataset, and the model is tested with a different dataset. The model selection is performed with the square of the errors.
PubDate: 2023-06-04
- Modelling local climate change using site-based data
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Abstract: In the context of the ongoing United Nations Framework Convention on Climate Change (UNFCCC) process, it seems important to focus attention not only on global mean surface air temperature (GSMT) but also on the climate of specific regions in order to gain insights into the dynamics of the changes, the timescales of the periodic components, the local trends and the relationships between climatic variables in the region of interest. This is important for scientists as well as for policymakers. This paper provides an analysis of the changes in local air temperature and precipitation depth in exceptionally long observational records and examines the relationships between these two variables. The focus is on monthly values. Temperature maximum, minimum, range, and cumulative precipitation depth are considered. The wavelet analysis shows that the scale of variation is different for temperature and precipitation and that the behavior of the temperature range values diverges from the behavior of the minimum and maximum values. The timescale of important changes in the long-term trend is, however, similar. Results also suggest that the main mode of variability is persistent through time in the series of temperature maximum, minimum, and range but not in precipitation depth. This is a clear evidence of climate change. All series show variances that change over time and are, as expected, nonstationary. The analysis of the wavelet coherence shows that the relationship between precipitation and temperature evolves through time, and its intensity varies considering different time scales. The association between these climatic variables is particularly strong in the last decade. Is it noteworthy that the analysis of the coherence suggests that temperature is leading to rain and not the other way around. This highlights the impact of global warming on the hydrologic cycle and on related human activities.
PubDate: 2023-06-03
- A Gibbs sampler for multi-species occupancy models
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Abstract: Multi-species occupancy (MSO) models use detection-nondetection data from species observed at different locations to estimate the probability that a particular species occupies a particular geographical region. The models are particularly useful for estimating the occupancy probabilities associated with rare species since they are seldom observed when undertaking field surveys. In this paper, we develop Gibbs sampling algorithms that can be used to fit various Bayesian MSO models to detection-nondetection data. Bayesian analysis of these models can be undertaken using statistical packages such as JAGS, Stan, and NIMBLE. However, since these packages were not developed specifically to fit occupancy models, one often experiences long run-times when undertaking analysis. However, we find that these packages that were not developed specifically to fit MSO models are less efficient than our special-purpose Gibbs sampling algorithms.
PubDate: 2023-05-30
- A multi-dimensional non-homogeneous Markov chain of order K to jointly
study multi-pollutant exceedances-
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Abstract: In this work we consider a multivariate non-homogeneous Markov chain of order \(K \ge 0\) to study the occurrences of exceedances of environmental thresholds. In the model, \(d \ge 1\) pollutants may be observed and, according to their respective environmental thresholds, a pollutant’s concentration measurement may be considered an exceedance or not. The parameters of the model are the order of the chain, and its initial and transition distributions. These parameters are estimated under the Bayesian point of view with the maximum a posteriori and leave-one-out cross validation methods used to estimate the order. In the case of the initial and transition probabilities, the estimation is made through samples generated using their respective posterior distributions. Once these parameters are obtained, we may estimate the probability of having no, one or more pollutants exceeding the associated environmental thresholds. This is made using the Markov property as well as a recurrence formula. Results are applied to the case where \(d = 2\) which will correspond to ozone and particulate matter with diameter smaller than 10 microns (PM \(_{10}\) ) measurements obtained from the Mexico City monitoring network.
PubDate: 2023-04-13
- Influence diagnostics in Gaussian spatial–temporal linear models
with separable covariance-
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Abstract: In recent decades, there has been a growing interest in modeling spatial–temporal data, which can be found in many fields including geoscience, meteorology and ecology, among many others. The spatial–temporal dependence structure modeling, using a random field approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. Our proposal is to extend the results of Uribe-Opazo et al. (J Appl Stat 39:615–630, 2012) and De Bastiani et al. (Test 24:322–340, 2015) in the studies of diagnostic techniques to assess the sensitivity of the maximum likelihood estimators to small perturbations in the response variable for the spatial–temporal linear models with separable covariance. The method’s viability is illustrated in a simulation study, and in an application to eggs anchovy (Engraulis ringens) abundance data in ichthyoplankton surveys from the northern zone of Chile. The results show that the proposed methodology allows to detect influential observations in a spatial-temporal data set when their covariances are separable.
PubDate: 2023-04-01
- Design-based spatial interpolation with data driven selection of the
smoothing parameter-
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Abstract: In the inverse distance weighting interpolation the interpolated, value is a weighted mean of the sampled values, with weights decreasing with the distances. The most widely adopted class of distance functions is the class of negative powers of order \(\alpha \) and the appropriate choice of the smoothing parameter \(\alpha \) is a crucial issue. In this paper, we give sufficient conditions for the design-based consistency of the inverse distance weighting interpolator when \(\alpha \) is selected by cross-validation techniques, and a pseudo-population bootstrap approach is introduced to estimate the accuracy of the resulting interpolator. A simulation study is performed to empirically confirm the theoritical findings and to investigate the finite-sample properties of the interpolator obtained using leave-one-out cross-validation. Moreover, a comparison with the nearest neighbor interpolator, which is the limiting case for \(\alpha =\infty \) , is performed. Finally, the estimation of the surface of the Shannon diversity index of tree diameter at breast height in the experimental watershed of Bonis forest (Southern Italy) is described.
PubDate: 2023-02-16
DOI: 10.1007/s10651-023-00555-w
- Measuring differences in efficiency in waste collection and disposal
services from the EU targets in Campania municipalities-
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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
DOI: 10.1007/s10651-022-00554-3
- Impact of social integration and government support on ecological
immigrants’ vulnerability to poverty-
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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
DOI: 10.1007/s10651-022-00553-4
- 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: 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
DOI: 10.1007/s10651-022-00551-6
- The impacts of migrants on environmental degradation in developing
countries-
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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
DOI: 10.1007/s10651-022-00552-5
- Nonparametric conditional density estimation in a deep learning framework
for short-term forecasting-
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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: 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
DOI: 10.1007/s10651-022-00550-7
- 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: 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
- Some efficient closed-form estimators of the parameters of the generalized
Pareto distribution-
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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: 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: 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
- 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: 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: 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
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