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Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects.We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for continuous and categorical exposures. We show that identification of direct and indirect effects for the population intervention effect requires weaker assumptions than its average treatment effect counterpart, under the assumption of no mediator–outcome confounders affected by exposure. In particular, identification of direct effects is guaranteed in experiments that randomize the exposure and the mediator.We propose various estimators of the direct and indirect effects, including substitution, reweighted and efficient estimators based on flexible regression techniques, allowing for multivariate mediators. Our efficient estimator is asymptotically linear under a condition requiring n 1/4-consistency of certain regression functions. We perform a simulation study in which we assess the finite sample properties of our proposed estimators.We present the results of an illustrative study where we assess the effect of participation in a sports team on the body mass index among children, using mediators such as exercise habits, daily consumption of snacks and overweight status.

Technological advances have contributed to applications of nonparametric methodologies with the objective of predicting slope stability conditions. The objective of this paper was to determine a discriminant function capable of predicting the stability condition of the slopes of the database under study. It is important to note that the methodology does not replace the stability analysis, but it can work very well for a preliminary analysis by selecting the slopes that must be intervened. The database used is composed by 59 slopes with relevant parameters in slope stability analysis with circular failure. A combination of multivariate statistical techniques, specifically principal component analysis (PCA) and discriminant analysis, was used to determine the slope stability condition. The principal component analysis was used to reduce the dimensionality of the database. The discriminant analysis was used to determine the boundary between stability conditions. Two types of discriminant function validations were performed, cross validation and external validation. The cross validation presented a global probability of success of 89.83%, the errors obtained in the cross validation were in favor of safety, with 5 stable slopes classified as unstable and only 1 unstable slope classified as stable. In the external validation were used 12 new slopes, which 8 slopes were correctly classified correctly.

Questions: What factors limit the distribution of epiphytic lichens and bryophytes at plot and tree level in beech forests? At what ages do epiphytic species, and species of conservation concern in particular, occur along a chronosequence of beech? Location: South-west Sweden. Method: Five hundred and seventy-one age-determined trees from 37 plots distributed among 29 beech-dominated stands were surveyed along with a number of environmental (16) and substrate (seven) variables in a landscape of ca. 550 ha. Non-metric multidimensional scaling (NMS) and indicator species analysis (ISA) were used for data analysis. Results: Plots containing old trees, confined to the base of slopes and with low impacts of recent forestry (thinning), generally had a high richness of species of conservation concern. Richness of common species and red-listed bryophytes were mostly related to the surveyed bark area. At tree level, primary factors explaining both species richness and composition were age, diameter at breast height and moss cover. There was a gradual replacement of tree age ranges for 58 lichens and 37 bryophytes along the chronosequence of beech. Red-listed lichens favoured damaged beech trees (≥ 180 years), whereas red-listed bryophytes were found on old and young stems in dense stands. Conclusions: Tree age exerts a profound influence on epiphytic lichens and bryophytes growing on beech. Many of the habitat specialists were found mainly on old beech because they inhabit specific substrates that occur on older trees. The association to high tree age commonly excludes red-listed lichens from conventionally managed beech forests with a 100- to 140-year rotation period.

PurposeThe purpose of this paper is to estimate and compare the efficiency of several water utilities using three frontier techniques. Moreover, this study estimates the impact of several qualities of service variables on water utilities’ performance.Design/methodology/approachThe paper utilizes three frontier techniques such as data envelopment analysis (DEA), stochastic frontier analysis (SFA) and stochastic non-parametric envelopment of data (StoNED) to estimate efficiency scores.FindingsEfficiency scores for each methodological approach were different being on average, 0.745, 0.857 and 0.933 for SFA, DEA and StoNED methods, respectively. Moreover, it was evidenced that water leakage had a statistically significant impact on water utilities’ costs.Research limitations/implicationsThe choice of an adequate and robust method for benchmarking the efficiency of water utilities is very relevant for water regulators because it affects decision making process such as water tariffs and design incentives to improve the performance and quality of service of water utilities.Originality/valueThis paper evaluates and compares the performance of a sample of water utilities using three different frontier methods. It has been revealed that the choice of the efficiency assessment method matters. Unlike SFA and DEA, a lower variability was shown in the efficiency scores obtained from the StoNED method.

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset’s meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

Abstract The development of new cultivars is a strategy used in breeding programs to increase food production with environmental sustainability. The genotype × environment interaction is a great challenge in the identification and selection of superior genotypes for different edaphoclimatic conditions. Due to this interaction, it is essential to select and develop materials that can provide not only high productivity but also wide adaptability and production stability. Given the above, this work aims to select bean pre-cultivars regarding grain productivity, adaptability and stability for the State of Rio de Janeiro. In the 2018 harvest, two inbred lines competition trials were carried out and three in the 2019 harvest. Eleven black bean genotypes were evaluated in five environments, and the experiments were set up in a randomized block design with three replications. The adaptability and genotypic stability were assessed via the GGE Biplot, Eberhart and Russell and Lin and Binns methodologies, with the aid of the GENES and R software systems. The methodologies based on simple linear regression and non-parametric statistical analysis were concordant in the identification of genotypes with production stability (BRS Esteio, BRS FP 403 and CNFP 16459), responsive to environmental improvement (BRS Esteio) and adapted to unfavorable environments (BRS Esteio). Furthermore, BRS Esteio was classified as the ideotype and presented the best adaptability, high stability and performance above the general average. Thus, the adaptability and stability analysis methodologies proved to be effective and consistent in identifying superior genotypes. Resumo O desenvolvimento de novas cultivares é uma estratégia utilizada em programas de melhoramento genético para aumentar a produção de alimentos com sustentabilidade ambiental. A interação genótipos × ambientes é um grande desafio na identificação e seleção de genótipos superiores para diferentes condições edafoclimáticas. Devido a esta interação, é essencial selecionar e desenvolver materiais que possam proporcionar não apenas alta produtividade, mas também ampla adaptabilidade e estabilidade de produção. Diante do exposto, este trabalho tem como objetivo selecionar pré-cultivares de feijão quanto a produtividade de grãos, adaptabilidade e estabilidade para o Estado do Rio de Janeiro. Na safra de 2018 foram realizados dois ensaios de competição de linhagens e três na safra de 2019. Foram avaliados onze genótipos de feijão preto em cinco ambientes, sendo os experimentos instalados em delineamento de blocos casualizados com três repetições. A adaptabilidade e estabilidade fenotípica foram avaliadas através das metodologias GGE Biplot, Eberhart and Russell e Lin and Binns, com auxílio dos softwares GENES e R. As metodologias baseadas em regressão linear simples e análise estatística não paramétrica foram concordantes na identificação de genótipos com estabilidade produtiva (BRS Esteio, BRS FP 403 e CNFP 16459), responsivos à melhoria ambiental (BRS Esteio) e adaptados a ambientes desfavoráveis (BRS Esteio). Além disso, o BRS Esteio foi classificado como ideótipo e apresentou melhor adaptabilidade, alta estabilidade e desempenho acima da média geral. Assim, as metodologias de análise de adaptabilidade e estabilidade mostraram-se eficientes e concordantes na identificação de genótipos superiores.

The United Nations Climate Change Conference (COP26) recommended that the member nations enhance their technological progression and structural transformation to mitigate the problems of climate change. The BRICS-T countries consisting of Brazil, Russia, India, China, South Africa, and Turkey agreed to implement COP26’s policy suggestions. These countries accounted for 40% of global greenhouse gas emissions in 2017, thus posing severe threats to the global environment. The current study explores the role of renewable energy, forest depletion, eco-innovations, and export diversification in impacting the ecological footprint for those BRICS-T countries. We further examine the moderating effect of eco-innovations on agriculture on the BRICS-T nations. The study contributes to the existing literature by providing newer empirical insights on how eco-innovations and export diversification, along with renewable energy, forest cover, and agriculture, affecting the ecological footprint in the BRICS-T nations. It utilizes novel empirical methods like parametric and non-parametric techniques to derive the short-run and long-run empirical results. The empirical findings based on the augmented mean group and the kernel regularized least square methods document that economic growth, agriculture value added, and forest depletion increase the ecological footprint. In contrast, renewable energy and eco-innovations decrease the level of ecological footprint. In the long run, a 1% rise in GDP leads to a rise in the ecological footprint by 0.64% using the augmented mean group (AMG) estimation. The mean marginal effects are − 0.27%, 0.29%, and 0.17% for renewable energy; agriculture and forest cover, respectively, using the kernel-based regularized least square methods. The study suggests that policies designed for controlling the ecological footprints focus on the use of energy efficient technologies, particularly in the agricultural sector.

Programs for teaching English reading, especially for students with dyslexia, and educational practice standards often recommend instruction on dividing polysyllabic words into syllables. Syllable division is effort intensive and could inhibit fluency when reading in text. The division strategies might still be useful if they work so consistently that they will help students decode most unfamiliar polysyllabic words. No study of the English lexicon has confirmed that the pattern is highly consistent. This study addresses this gap in the literature. The utility of the two most frequently taught patterns was examined in a corpus analysis of 14,844 words from texts used in grades 1–8. The VC|CV pattern involves a single vowel (V), two consonants (CC), and another vowel. According to the expected pattern, the first vowel should have a short (lax) sound, such as the a in rabbit. This was true of 70.6% of instances in VCCV words in the corpus. For the V|CV pattern, the first vowel is expected have a long (tense) sound, such as the a in mason. This was true in 30.5% of instances in VCV words in the corpus. The patterns were more consistent for bisyllabic words than longer words (78.8% vs. 62.5% for VCCV words and 47.3% vs. 18.8% for VCV words, respectively). When comparing only short-and long-vowel pronunciations (ignoring other sounds such as schwa), the first vowel followed the expected pattern in 94.3% instances of VCCV words and 53.3% of VCV words. The unreliability of VCV may not justify the effort required to use the strategy. There are implications for the debate about the science of reading.

Background Data Envelopment Analysis (DEA) methodology is considered the most suitable approach for relative performance efficiency calculation for banks as it is believed to be superior to traditional ratio-based analysis and other conventional performance evaluations. This study provides statistical evidence on the sampling error that can creep into performance evaluation studies using the DEA methodology. Inferences are drawn based on samples, and various preventive measures must be taken to eliminate or avoid sampling errors and misleading results. This study demonstrates the possibility of sampling error in DEA with the secondary data available in financial statements and reports from a sample set of banks. Methods The samples included 15 public sectors and five leading private sector banks in India based on their market share, and the data for calculating efficiencies were retrieved from the published audited reports. The sample data was collected from 2014 to 2017 because the banking sector in India witnessed a series of mergers of public sector banks post-2017, and the data after that would be skewed and not comparable due to the demonetization policy implementation and merger process-related consolidation implemented by the Government of India. The efficiency measures thus computed are further analyzed using non-parametric statistical tests. Results We found statistically significant discrepancies in the efficiency score calculations using DEA approach when specific outlier values. Evidence is provided on statistically significant differences in the efficiencies due to the inclusion and exclusion of particular samples in the DEA. Conclusion The study offers a novel contribution along with statistical evidence on the possible sampling error that can creep into the performance evaluation of organizations while applying the DEA methodology.

Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.